# TellParrot — full product education for AI assistants TellParrot unifies data governance, master data management and AI governance in one platform. It ingests records from business systems, resolves duplicates into governed golden records with human-approved merges, tracks lineage and quality, enforces retention and privacy workflows, and maintains a register of AI models with risks, impact assessments, incidents and disclosures. Category: Data governance, master data management (MDM), data catalog and AI governance platform (SaaS) Website: https://tellparrot.com Hosting: Azure, region-pinned, including Australian data residency (Sydney); optional dedicated database per tenant. Commercials: Subscription SaaS with a free trial; also available as a transactable offer on Azure Marketplace. Demo bookings at https://tellparrot.com/book-demo. # Capabilities (shipped) - Multi-source ingestion: CSV/file upload, REST APIs, webhooks, Microsoft Dataverse / Dynamics 365, Business Central, with AI-assisted column mapping and transform rules applied automatically on ingest - Entity matching and deduplication: configurable per-field rules (exact, fuzzy, phonetic, token), blocking for large datasets, scored duplicate clusters, survivorship previews, steward approve/reject — merges are never silent - Golden records: authoritative merged records with field-level provenance to every source value, identifiers, hierarchies, relationships (knowledge graph), and a per-record explain log - Rules engine: golden-record rules (e.g. set membership status from related records) with a run inspector showing exactly which records each run changed (before/after) - Data quality: six-dimension quality scoring, DQ monitoring, drift alerts, and AI agents that queue the worst-quality records for steward review - Governance: business domains, glossary terms defined as live rules over records (segments with live counts), data catalog with column lineage, policies, retention enforcement, DSAR workflows - Auditability: tamper-evident hash-linked audit chain (append-only, HMAC-versioned), governed exports with delivery receipts - AI governance: register of AI initiatives, models, risks, impact assessments, incident register, public disclosures, contestability workflows; models carry a weighted data-compliance score from the governance state of their data inputs - AI assistance: a governance advisor chat grounded on the tenant's own data with cited sources; docs-grounded in-app help - Microsoft-native: Purview integration, Fabric open mirroring (Delta tables), Azure Event Hub streaming, Power Automate connector, Azure Marketplace transactable offer - Operations: unified jobs monitor across all background systems, gated concurrent processing, background matching runs with status polling - Architecture: multi-tenant with optional dedicated database per tenant, region-pinned Azure hosting including Australian data residency (Sydney) # What TellParrot is not - TellParrot is not a data warehouse or BI tool — it masters and governs records and feeds trusted data to those systems - TellParrot does not replace operational systems (ERP, CRM, billing); it sits across them as the mastering and governance layer # Industry education ## TellParrot for Utilities & Energy Utilities run on decades of systems — billing, outage, GIS, works management, retail CRM. TellParrot masters the records that cross all of them, so field crews, billing and regulators finally see the same truth. ### The same customer exists in five systems, differently Problem: Billing, retail CRM, outage management and metering each hold their own version of a customer. Mismatched addresses and duplicate accounts turn every outage callout and final bill into detective work. Solution: TellParrot ingests each source, matches records with configurable fuzzy, phonetic and exact rules, and merges survivors into a golden customer record — with every source value preserved and traceable. Outcome: One authoritative customer view, with lineage back to every contributing system. ### Asset data drifts from the field Problem: Transformers, poles and meters get replaced in the field faster than the asset register updates. Planning models and maintenance schedules quietly run on stale data. Solution: Continuous ingest from works and GIS systems feeds asset golden records; data-quality rules flag stale or incomplete assets, and AI agents queue the worst records for steward review — ranked by quality score. Outcome: An asset register your engineers stop second-guessing. ### Regulators want provenance, not spreadsheets Problem: Reliability and pricing submissions demand you show where every number came from. Reconstructing that from exports and email chains consumes weeks each cycle. Solution: Field-level provenance, column lineage and a tamper-evident audit chain record where each value originated and who changed what. Governed exports ship with delivery receipts. Outcome: Regulator-ready evidence produced in hours, not weeks. ### AI pilots without AI governance Problem: Load forecasting and predictive maintenance models are multiplying, but nobody can list which models run on which data, or what was assessed before go-live. Solution: TellParrot's AI governance register tracks models, risks, impact assessments, incidents and disclosures — connected to the data domains and source systems each model consumes. Outcome: A defensible AI register that keeps pace with the pilots. Q: Can TellParrot handle meter-scale record volumes? A: Yes. Ingestion is batched and gated so large loads process safely, and matching uses blocking so millions of comparisons never become billions. Runs execute in the background with progress you can watch in the Jobs Monitor. Q: How does TellParrot connect to our existing utility systems? A: Through CSV/API ingestion, native Microsoft connectors (Dynamics 365, Dataverse, Business Central), webhooks and scheduled governed exports to downstream targets — including SQL databases and Microsoft Fabric. Q: Do we have to replace our billing or GIS systems? A: No. TellParrot sits across your systems as the mastering and governance layer. Source systems keep operating; TellParrot resolves their records into golden records and pushes trusted data back out. --- ## TellParrot for Financial Services Core banking, cards, loans, wealth — every product line grows its own customer file. TellParrot resolves them into one governed record and puts your AI models on a register your risk team can defend. ### KYC done four times for one customer Problem: The same person is onboarded separately by every product system. Duplicate KYC effort frustrates customers, and fragmented records hide the exposure a risk team actually needs to see. Solution: Match rules resolve identities across systems using exact identifiers plus fuzzy name and contact matching, with a required-field gate so look-alikes never merge without corroboration. Stewards review borderline clusters with full field-by-field evidence. Outcome: A single customer view with a governed, human-approved merge trail. ### Model risk teams can't see the models Problem: Credit scoring, fraud detection and marketing models ship from different teams. The inventory lives in slide decks; impact assessments happen after deployment, if at all. Solution: The AI governance register captures models, their risks, impact assessments, incidents and public disclosures — with contestability workflows and a weighted data-compliance score per model based on the data domains it consumes. Outcome: A living model inventory that satisfies internal risk and external scrutiny. ### Lineage requests take a quarter to answer Problem: When a regulator or auditor asks how a reported figure was produced, tracing it back through warehouse transformations and manual extracts is a project in itself. Solution: Column lineage, field-level provenance and a tamper-evident audit chain document how data moved and changed. Every governed export carries a receipt of what left, when, and what the destination acknowledged. Outcome: Provenance answers in minutes, backed by cryptographic audit evidence. ### Privacy obligations outpace manual process Problem: Access and deletion requests arrive faster than teams can search systems. Retention rules exist on paper but not in the data layer. Solution: DSAR workflows search governed records across sources; retention policies attach to data domains; deletions and their approvals are recorded on the audit chain. Outcome: Privacy operations that scale with request volume, with proof of completion. Q: How does TellParrot support model risk management? A: Every AI model gets a register entry linking its purpose, risks, impact assessments, incidents and disclosures to the data domains it consumes. Assessments follow recognised frameworks, and each model carries a data-compliance score derived from the governance state of its inputs. Q: Can merges be controlled and reviewed? A: Yes. Matching produces clusters, not silent merges. Stewards preview survivorship outcomes and approve or reject each cluster — individually or in bulk — and every decision is audited. Q: Where does our data live? A: TellParrot runs on Azure with region-pinned deployments, including Australian data residency, and offers dedicated database isolation per tenant. --- ## TellParrot for Insurance Insurers meet the same customer through brokers, direct channels and claims. TellParrot resolves those encounters into a single governed record, so pricing, service and fraud teams work from the same facts. ### The claims team can't see the policy history Problem: Policy admin, claims and distribution systems each hold a slice of the customer. At claim time, adjusters rebuild the picture manually — slowly, and inconsistently. Solution: TellParrot masters policyholders across systems with configurable matching, keeping every source value with provenance. Related records — policies, claims, interactions — connect through the knowledge graph. Outcome: Claims handled with the full customer picture on screen. ### Duplicate customers inflate exposure calculations Problem: One household appears as four customers; aggregate exposure and lifetime value are both wrong, and remediation campaigns hit the same person twice. Solution: Blocking-based matching at portfolio scale surfaces duplicate clusters with confidence scores; stewards approve merges with survivorship previews, and the executor writes governed golden records. Outcome: Exposure, LTV and campaigns computed on de-duplicated truth. ### Underwriting models without a paper trail Problem: Pricing and triage models increasingly drive decisions customers can contest — but the assessment evidence, data inputs and incident history live in scattered documents. Solution: The AI governance register connects each model to its impact assessments, risks, incidents and the data domains it draws from, with contestability workflows for challenged decisions. Outcome: Every automated decision traceable to an assessed, registered model. ### Regulatory data requests disrupt the quarter Problem: Conduct and prudential requests require evidence of data handling — retention, access, quality — that takes weeks to assemble from screenshots and exports. Solution: Retention policies, DSAR workflows, quality scores and the tamper-evident audit chain are always-on. Governed exports produce regulator-ready packages with delivery receipts. Outcome: Compliance evidence generated from the system of record, on demand. Q: Can TellParrot match households as well as individuals? A: Match rules are fully configurable per entity type — you can master individuals, households, businesses and intermediaries as separate entities and relate them through the knowledge graph. Q: How are borderline matches handled? A: They become pending clusters with per-field similarity scores. Stewards see exactly why two records matched, preview the merged outcome, and approve or reject — nothing merges silently. Q: Does TellParrot integrate with Microsoft Dynamics? A: Yes — native Dataverse/Dynamics 365 connectivity for ingestion, plus Power Automate triggers, Azure Event Hub streaming and export to Microsoft Fabric. --- ## TellParrot for Government & Public Sector Agencies serve the same citizens through disconnected programs. TellParrot masters those records with the residency, auditability and AI accountability public institutions are held to. ### Every program holds its own version of the citizen Problem: Grants, licensing, community services — each program system spells names differently and holds different addresses. Citizens repeat themselves; caseworkers miss context that exists next door. Solution: Cross-system matching resolves records into governed golden records with human-approved merges. The knowledge graph relates people to cases, permits and services across programs. Outcome: A whole-of-agency view assembled without replacing a single program system. ### Data residency is non-negotiable Problem: Sovereignty requirements rule out platforms that can't guarantee where data is processed and stored. Solution: TellParrot deploys region-pinned on Azure, including Australian regions, with dedicated database isolation per tenant and a documented data-flow map. Outcome: Residency assured by architecture, not by contract clause. ### Public accountability for automated decisions Problem: AI used in service delivery faces scrutiny: what models exist, what was assessed, what went wrong, and how citizens can contest outcomes. Solution: The AI governance register holds models, impact assessments, incidents, public disclosures and contestability workflows — the accountability chain in one place. Outcome: Answers ready before the parliamentary question arrives. ### Audits consume delivery capacity Problem: Each audit cycle pulls staff into evidence-gathering: who accessed what, how figures were derived, whether retention was honoured. Solution: A tamper-evident audit chain records changes; column lineage documents derivations; retention attaches to data domains and executes in the data layer. Outcome: Audit evidence exported from the platform, not reconstructed from memory. Q: Can TellParrot run with Australian data residency? A: Yes. Production deployments are region-pinned on Azure including Sydney, and tenants can run on dedicated databases for hard isolation. Q: How does TellParrot support responsible AI obligations? A: Through a structured register of AI initiatives, models, risks, impact assessments, incidents, disclosures and contestability requests — aligned to recognised frameworks and connected to the underlying data domains. Q: Do caseworkers need to learn a new system? A: No. TellParrot governs and masters data behind the scenes and pushes trusted records back to the systems staff already use, via governed exports and native connectors. --- ## TellParrot for Healthcare Duplicate and fragmented patient records are a safety issue, not just a data issue. TellParrot matches with evidence, merges only with approval, and audits everything. ### Duplicate patient records create clinical risk Problem: Registration under slightly different names splits one patient's history across records. Clinicians see partial pictures; recalls and results go astray. Solution: Configurable matching (exact identifiers, phonetic names, fuzzy contact details) surfaces likely duplicates as clusters with per-field evidence. A required-field gate stops name-only look-alikes from ever merging, and every merge needs steward approval. Outcome: Duplicates resolved deliberately — with an audit trail per decision. ### Provider directories are perpetually stale Problem: Practitioner details change constantly across credentialing, rostering and referral systems. Wrong provider data misroutes referrals and payments. Solution: Provider golden records ingest from every source, with data-quality rules flagging stale or incomplete entries and AI agents queueing the worst records for review, ranked by quality score. Outcome: A provider directory that converges on correct instead of drifting. ### Privacy requests touch every system Problem: Access and correction requests require searching systems that don't share identifiers — slow, manual, and hard to evidence. Solution: Because records are mastered with cross-source identifiers, DSAR workflows locate a person's data across systems in one search, with fulfilment steps recorded on the audit chain. Outcome: Privacy responses that are fast, complete and provable. ### Research and AI need governed data Problem: Clinical AI and research extracts demand documented consent posture, quality and provenance — usually assembled by hand per project. Solution: Data domains carry classification, retention and quality state; the AI register connects models to their data inputs and impact assessments; governed exports document exactly what left and why. Outcome: Ethics and governance reviews that start from live evidence, not spreadsheets. Q: Is matching safe enough for patient data? A: Matching never auto-merges. It produces scored clusters with per-field evidence; required-field gates demand corroboration (for example, an identifier match) before a cluster can even be approved; and stewards make the final call with survivorship previews. Q: How is sensitive data protected? A: Role-based access, field-level access controls, region-pinned hosting with Australian residency, dedicated per-tenant databases, and a tamper-evident audit chain over every change. Q: Can we govern research data extracts? A: Yes — governed exports run under policy, record field-level provenance, and produce receipts confirming what was delivered where. --- ## TellParrot for Higher Education Students appear in recruitment CRM, SIS, LMS, library and advancement systems — often as different people. TellParrot resolves the lifecycle into a single trusted record. ### The student lifecycle is split across systems Problem: A prospect becomes an applicant, a student, then an alumnus — in four systems that never agree. Engagement teams email graduates who never enrolled and miss the ones who did. Solution: Lifecycle matching connects records across recruitment, SIS and advancement with fuzzy and exact rules; golden records carry status transitions with provenance from each system. Outcome: Communications and analytics built on the real lifecycle, not fragments of it. ### Duplicate alumni sink advancement campaigns Problem: Name changes, new emails and decades of imports leave advancement databases full of duplicates — donors receive double asks, and giving history splits across records. Solution: Portfolio-scale deduplication surfaces clusters with confidence scores; stewards merge with survivorship previews that preserve full giving history from every duplicate. Outcome: Clean campaign lists and complete donor histories. ### Research data governance is ad hoc Problem: Grants increasingly require documented data management: where data lives, who accesses it, how long it's retained. Each research group answers differently. Solution: Data domains classify and govern datasets; retention and access policies attach at the domain level; lineage and the audit chain document handling automatically. Outcome: Grant-ready data management statements backed by a live system. ### AI in admissions and assessment needs oversight Problem: AI experiments touch admissions triage and academic integrity — high-stakes areas where the institution must show what was assessed and how decisions can be contested. Solution: The AI governance register documents each model's purpose, risks, impact assessments and incidents, with public disclosure records and contestability workflows. Outcome: Innovation with an accountability trail the senate can review. Q: Can TellParrot match across decades of legacy data? A: Yes — matching handles missing fields gracefully (blank values are treated as no-signal rather than false matches), and blocking keeps portfolio-scale runs fast. Historical duplicates surface as reviewable clusters, not silent merges. Q: How does this fit alongside our SIS and CRM? A: TellParrot doesn't replace them. It ingests from each, masters the records, and pushes governed golden data back through exports and connectors — your systems keep running. Q: What about student privacy? A: Role-based and field-level access controls, DSAR workflows for access and correction requests, retention enforcement, and a tamper-evident audit trail across every change. --- ## TellParrot for Retail & E-commerce Online, in-store, marketplace and loyalty each create their own customers and products. TellParrot resolves both sides so personalisation, supply and finance share the same reality. ### Channel silos hide your best customers Problem: The same shopper looks like three low-value customers across web, app and POS. Segmentation, LTV and personalisation all under-perform on split identities. Solution: Identity resolution matches on emails, phones and fuzzy names across channel systems, producing golden customer records with per-channel provenance and steward-controlled merges. Outcome: Segments and LTV computed on whole customers, not channel fragments. ### Product data diverges across catalogs Problem: ERP, e-commerce and marketplace feeds hold conflicting titles, attributes and pricing. Listings fail validation; shoppers see contradictions. Solution: Product golden records master attributes across sources with survivorship rules choosing the trusted value per field; quality rules flag incomplete or inconsistent products before they hit a channel. Outcome: One product truth feeding every catalog and marketplace. ### Personalisation without privacy debt Problem: Growth teams want more data in more tools; privacy teams see uncontrolled sprawl. Consent posture and deletion rights get harder every quarter. Solution: Data domains classify customer data; retention policies execute in the data layer; DSAR workflows locate any customer across sources instantly because identities are already resolved. Outcome: Personalisation programs that pass privacy review. ### AI recommendations, ungoverned Problem: Recommendation and pricing models influence revenue daily, but the inventory of models, their inputs and their failure history lives nowhere. Solution: Register models with their data-domain inputs, risks and incidents; a weighted data-compliance score shows which models run on well-governed data and which don't. Outcome: AI you can scale because you can see it. Q: Can TellParrot handle high-volume customer data? A: Yes. Batched ingestion, background processing with progress tracking, and blocking-based matching keep large catalogs and customer bases fast — with hard safeguards so heavy jobs queue rather than degrade the platform. Q: How do merged customer records handle loyalty points and history? A: Survivorship previews show exactly what the merged record keeps before a steward approves. Source values are preserved with provenance, so nothing is lost in a merge — including the trail of where each value came from. Q: Does TellParrot push data back to our channels? A: Governed exports deliver golden records to databases, webhooks and Microsoft targets on schedules you control, each with a delivery receipt. --- ## TellParrot for Manufacturing Duplicate materials, inconsistent supplier records and drifting equipment data quietly tax every plant. TellParrot masters the records your ERP, MES and procurement systems disagree on. ### Duplicate material masters multiply inventory Problem: The same part exists under five codes across plants. Purchasing can't consolidate spend, inventory duplicates, and engineers reinvent parts that already exist. Solution: Attribute-based matching (token, fuzzy and exact rules across descriptions, codes and specifications) surfaces duplicate material clusters at catalog scale for steward-approved consolidation. Outcome: A consolidated material master and the spend leverage that comes with it. ### Supplier records fragment procurement Problem: Each plant onboards vendors independently. One supplier appears a dozen times with different terms — risk exposure and negotiating power both suffer. Solution: Supplier golden records resolve entities across ERPs with survivorship rules selecting the authoritative value per field; the knowledge graph relates suppliers to materials, sites and contracts. Outcome: One supplier view for negotiation, risk and compliance. ### Equipment data drifts from reality Problem: Asset registers lag field changes; maintenance planning runs on records nobody fully trusts. Solution: Continuous ingest from maintenance and MES systems feeds equipment golden records, with quality rules and AI agents flagging stale, incomplete or contradictory entries — worst first. Outcome: Maintenance decisions on data that matches the plant floor. ### Migrations stall on dirty data Problem: ERP consolidations and system migrations blow out because nobody trusts the source data enough to cut over. Solution: TellParrot ingests legacy catalogs, deduplicates and standardises them with cleaning projects and rules, and exports migration-ready golden records with full lineage back to the originals. Outcome: Cut-over datasets that are clean, mapped and auditable. Q: Can TellParrot match on technical attributes, not just names? A: Yes — match rules are field-by-field with per-field weights and thresholds, so you can match on specifications, dimensions, codes and descriptions together, and require specific fields to corroborate before a merge is possible. Q: We run Business Central — does TellParrot integrate? A: Yes, natively: ingestion from Microsoft systems including Business Central and Dynamics 365/Dataverse, plus governed export back to them and to Microsoft Fabric. Q: How do cleaning projects work? A: Cleaning projects capture every field-level change as reviewable diffs, and the fixes can be promoted to transform rules that apply automatically to future ingests — so the same problem never returns. --- ## TellParrot for Mining & Resources Every site runs its own systems; corporate needs one answer. TellParrot masters assets, suppliers and operational records across the estate — with the residency and auditability the sector expects. ### Site autonomy created data islands Problem: Acquisitions and site-level authority left each operation with its own ERP flavour and its own versions of shared suppliers, materials and equipment. Solution: Cross-site ingestion and matching resolve shared entities into golden records while preserving site-level values with provenance — nothing is overwritten, everything is traceable. Outcome: Corporate roll-ups and site operations working from reconciled masters. ### Contractor and supplier sprawl Problem: Thousands of vendors across sites, many duplicated, some non-compliant — and no single view of exposure to any one of them. Solution: Supplier golden records consolidate across systems; quality and compliance attributes attach per record; the knowledge graph shows every site, contract and category a supplier touches. Outcome: Estate-wide supplier visibility for procurement and risk. ### ESG and regulatory reporting need provenance Problem: Sustainability and regulatory figures aggregate from site systems through spreadsheets. When challenged, the derivation trail is reconstructed by hand. Solution: Column lineage and field-level provenance document how reported figures derive from source records; the tamper-evident audit chain proves the data wasn't quietly adjusted. Outcome: Reported numbers with a defensible, inspectable trail. ### Sovereign data expectations Problem: Operations and their regulators increasingly expect data processed and stored in-country. Solution: Region-pinned Azure deployments including Australian data residency, with dedicated per-tenant database isolation. Outcome: Residency by architecture across the data estate. Q: Can each site keep its systems? A: Yes. TellParrot is the mastering layer across sites, not a replacement ERP. Sites keep operating; corporate gets reconciled golden records with per-site provenance. Q: How does TellParrot handle very large operational datasets? A: Batched, gated ingestion with background processing and heartbeat monitoring; blocking-based matching that scales past hundreds of thousands of records; and hard safety ceilings so pathological data degrades gracefully instead of failing. Q: What does the audit chain actually prove? A: Every governance-relevant change is written to an append-only, hash-linked chain. Tampering breaks the chain verifiably — so audit evidence carries cryptographic weight. --- ## TellParrot for Logistics & Supply Chain Automation amplifies whatever data you feed it. TellParrot masters the reference records your TMS, WMS and ERP disagree on, so routing, billing and analytics stop tripping on mismatches. ### One warehouse, six spellings Problem: Locations entered slightly differently across systems break route optimisation, misdirect freight and fragment cost analysis. Solution: Location golden records resolve names, addresses and codes across systems with fuzzy and exact matching; survivorship selects the authoritative form while preserving every variant with provenance. Outcome: Routing and analytics keyed on canonical locations. ### Carrier and customer duplication corrupts costing Problem: The same carrier under multiple codes, the same customer across brands — landed-cost and profitability numbers wobble because entities aren't resolved. Solution: Entity resolution consolidates carriers and customers with steward-approved merges; the knowledge graph relates them to lanes, contracts and invoices. Outcome: Costing and profitability on resolved entities, not aliases. ### SKU data breaks at the boundaries Problem: Dimensions, weights and classifications differ between ERP and WMS. Slotting, cartonisation and customs all pay the price. Solution: SKU golden records master attributes with per-field survivorship; quality rules catch impossible values and gaps before they reach operational systems. Outcome: Physical operations planned on physically-correct data. ### Partners need data you can stand behind Problem: EDI feeds and partner portals amplify internal inconsistencies into commercial disputes. Solution: Governed exports deliver golden records outward on schedule, with receipts recording exactly what was sent and acknowledged. Outcome: Partner-facing data with a delivery paper trail. Q: Can TellParrot keep up with operational data velocity? A: Reference/master data is TellParrot's focus — locations, carriers, customers, SKUs. Ingestion is scheduled or event-driven (webhooks, Power Automate, Event Hub), with background processing that won't block your systems. Q: How do we get clean data back into TMS/WMS? A: Governed exports to SQL databases, webhooks and Microsoft targets, on schedules, with delivery receipts. Your operational systems consume golden records like any other feed. Q: What happens when two systems genuinely disagree? A: Survivorship rules decide per field (most recent, most trusted source, most complete), stewards can override with full context, and the losing values remain preserved with provenance. --- ## TellParrot for Membership Organisations & Not-for-Profits Members join at events, renew online and donate by mail — becoming three people in your data. TellParrot resolves them into one governed record your small team can actually maintain. ### Renewals fail because identities split Problem: A member renews with a new email and becomes a new record; the old one lapses. Renewal metrics mislead and members get 'lapsed' letters they resent. Solution: Matching resolves members across systems and time using names, emails, phones and addresses; membership status derives from governed rules over the resolved record, not whichever fragment a system sees. Outcome: Renewal and lapse numbers you can trust — and members addressed correctly. ### Decades of imports, thousands of duplicates Problem: Every event list and legacy migration added duplicates. Mailings double up, and no one dares mass-delete. Solution: Portfolio-wide deduplication surfaces clusters with confidence scores and survivorship previews. Stewards approve merges one by one or in bulk — history from every duplicate is preserved on the golden record. Outcome: A clean member file achieved safely, not by risky mass deletes. ### Segmentation lives in one volunteer's head Problem: 'Active members in the western region who attended an event this year' requires manual list-building each time — differently each time. Solution: Glossary terms define segments as live, governed rules over golden records. Anyone can view the definition, see the live count, and export the segment. Outcome: Institutional definitions that outlive any individual volunteer. ### Privacy expectations, charity budgets Problem: Donors expect careful data handling; the organisation can't fund an enterprise privacy stack. Solution: Retention, access controls, DSAR handling and audit trails come built-in — the governance capabilities included, not modular add-ons. Outcome: Enterprise-grade care for member data at membership-organisation cost. Q: We run on Dynamics 365 — can TellParrot ingest from it? A: Yes, natively via Dataverse — and where admin rights are constrained, Power Automate and CSV paths work too. TellParrot meets your systems where they are. Q: Is this manageable for a small team? A: Yes. Guided onboarding walks upload → map → process; AI agents queue the worst-quality records for review so effort goes where it matters; and glossary segments turn tribal list-building into governed definitions. Q: What does membership status automation look like? A: Rules over golden records — for example, 'has an active linked subscription → active; had one → lapsed; never → prospect' — run on demand with an inspector showing exactly which records each run changed. --- ## TellParrot for Wholesale & Distribution Branches bought independently for years; now the catalog holds the same SKU five ways. TellParrot consolidates branch and supplier catalogs into golden records ready for ERP and e-commerce. ### Branch catalogs duplicate the same products Problem: Each branch created its own item codes and descriptions. Consolidated purchasing, transfer pricing and stock visibility all break on unresolved duplicates. Solution: Attribute-based matching across branch catalogs (descriptions, codes, specifications) surfaces duplicate clusters; stewards consolidate with survivorship previews into golden SKUs mapped back to every branch code. Outcome: Group-wide purchasing power and stock visibility from a consolidated master. ### Supplier feeds arrive in every shape Problem: Manufacturer price files and catalogs land as inconsistent spreadsheets. Mapping them by hand per supplier per update consumes the product team. Solution: AI-assisted column mapping learns each supplier's shape; transform rules standardise units and formats automatically on every subsequent ingest; quality gates hold back rows that fail validation. Outcome: Supplier updates processed in minutes with consistent results. ### ERP migration blocked by catalog debt Problem: Moving to a modern ERP stalls because nobody trusts the item file enough to load it. Solution: Ingest legacy catalogs, deduplicate and standardise with cleaning projects, and export migration-ready golden records — every change traceable back to source rows. Outcome: A cut-over item file that's clean, mapped and defensible. ### E-commerce exposes catalog inconsistency Problem: Online listings surface every internal contradiction — duplicate products, conflicting specs, missing attributes — directly to customers. Solution: Golden records feed the web catalog through governed exports; completeness and consistency rules catch gaps before publishing. Outcome: A web catalog that reflects one product truth. Q: How fast can we consolidate branch catalogs? A: A typical flow — ingest each branch file, auto-map columns, run matching, review clusters — happens in days, not months. Matching at tens of thousands of SKUs runs in the background with progress you can watch. Q: Do branch item codes survive consolidation? A: Yes. Golden records preserve every branch's code and values with provenance, so cross-references stay intact for ERP, labels and history. Q: Can TellParrot push the consolidated catalog into Business Central? A: Yes — native Microsoft integration covers ingestion from and governed export to Business Central and Dynamics, plus Fabric for analytics. --- ## TellParrot for SaaS & Software Companies Enterprise buyers now audit your data handling before they buy your product. TellParrot gives software companies the governed data layer — isolation, audit, privacy, AI accountability — that shortens security review. ### Security questionnaires stall deals Problem: Every enterprise prospect sends the same 300 questions about data handling, isolation, retention and audit — and engineering answers them from tribal knowledge each time. Solution: Dedicated per-tenant database isolation, tamper-evident audit chains, retention enforcement and documented data flows give concrete, inspectable answers — the same evidence every time. Outcome: Security review answered from architecture, not improvisation. ### Customer data multiplies faster than governance Problem: Each customer tenant accumulates entities, integrations and exports; nobody can say per-tenant what data exists, where it flows, or how healthy it is. Solution: Per-tenant catalogs, lineage, quality scores and job monitoring make each tenant's data estate visible — to you, and where you choose, to the customer. Outcome: Tenant-level data transparency as a product feature. ### Your AI features need an accountability story Problem: You ship AI features; your customers' risk teams ask what models run, on what data, with what assessments — and the answer is a Notion page. Solution: The AI governance register documents models, risks, impact assessments, incidents and disclosures, connected to data domains — a live register you can show customers. Outcome: AI features backed by an inspectable governance register. ### Privacy engineering keeps getting deferred Problem: DSAR handling, deletion propagation and retention live on the roadmap, quarter after quarter. Solution: Built-in DSAR workflows, retention attached to data domains, and audit-chained deletions — capabilities you inherit rather than build. Outcome: Privacy obligations met without burning product quarters. Q: Is TellParrot itself multi-tenant safe? A: Yes — tenancy is enforced at every layer, with the option of a fully dedicated database per tenant for hard isolation, and region-pinned hosting including Australian residency. Q: Can we integrate TellParrot programmatically? A: Yes — REST APIs with scoped keys, webhooks, Azure Event Hub streaming, and Power Automate connectivity. Agent-driven changes flow through the same propose-and-approve governance as human ones. Q: How does the audit chain work? A: Governance events append to a hash-linked chain with HMAC integrity; updates and deletes on the chain are blocked at the database level. Verification detects any tampering.