From assembling data to asking a question
The value was always locked inside the data. It just took hours of manual work to get it out. Talk to Data removes the assembly line between a business question and a trustworthy answer.
Drag the handle, or focus it and use the arrow keys.
One governed path from raw data to trusted answer
Four enterprise sources are unified into a governed data and knowledge layer, orchestrated by a single Planner Agent, and surfaced through one conversational interface — every layer platform-native and inside the enterprise network boundary.
Flow: the question enters the UI, the Planner routes it to the right specialist agent, the agent retrieves from governed data or the knowledge base, and a composed, sourced answer returns.
Two minds in one agent
The same assistant switches between precision and judgement. It knows when you need an exact number and when you need a recommendation.
The Data Specialist
For exact figures, the agent runs a deterministic query against the governed semantic model. It only answers when the question is unambiguous — metric, entity and time period all present.
- Returns precise, governed metrics, not approximations
- Resolves the exact entity before touching the data
- Refuses to guess: asks for what is missing instead
“What is the service rating for account 4821 in June?”
Ask it something and watch it work
The routing, the two modes and the validation gate are all things the system does rather than claims about it. Pick a question and follow it through.
A working model of the flow, with synthetic data. The third question is deliberately incomplete, so the gate fires instead of an answer — that behaviour is the point.
A single Planner conducting a team of specialists
One central Planner Agent interprets intent and delegates to the right domain specialist — a deliberately flat, observable topology engineered to stay reliable within real platform limits.
Planner Agent
Reads the question’s meaning, picks the right specialist, and composes one coherent, sourced answer from what comes back.
- Data RetrievalGoverned metrics from the semantic model
- Domain AgentRegulated-domain reasoning
- Account AgentAccount-intelligence reports
- OnboardingCustomer onboarding views
- CRMCRM via a secure middle layer
- KnowledgeSearch over curated documents
- One hop from question to specialist — deliberately flat
- Every route observable, so a wrong answer is traceable
- Engineered to stay inside real platform limits
Built so the answer is always the right answer
The biggest barrier to AI on enterprise data is trust. We engineered it in. The system would rather ask a question than risk a wrong one.
A validation gate, not a guess
Ambiguous questions are paused, not answered. The agent requires metric, entity and time before it queries, eliminating garbage-in, garbage-out.
The right entity, every time
A unique-identifier anchor maps each answer to the exact legal entity, so a number is never attributed to the wrong customer or facility.
Grounded and sourced
Answers are drawn from a single source of truth and a curated knowledge base — traceable, not improvised.
Enterprise security
Single sign-on with MFA, identity passthrough, and full network isolation keep every query inside the enterprise boundary.
Delivered, and running in production
Beyond the headline efficiency gain, the platform is live, passing user acceptance, with an enablement playbook in the hands of business users. Figures below are banded.
From kickoff to production in one quarter
A repeatable, low-risk path. The same method transfers to any data-rich enterprise.
- Phase 1
Align and architect
Kickoff, governance alignment, use-case prioritisation and architecture confirmation.
- Phase 2
Foundation
Cloud environment, repositories, CI/CD, platform setup and service configuration.
- Phase 3
Data and knowledge
Ingestion, semantic modelling, preparing data for AI, knowledge-base build and search indexing.
- Phase 4
Agents and orchestration
Planner flows, retrieval logic and backend integration across the agent and data platforms.
- Phase 5
Experience and UAT
Conversational UI, business walkthroughs, user acceptance testing and quality hardening.
- Phase 6
Go-live and handover
Final validation, documentation, enablement playbook and production readiness review.
The answers are already in your data.
Every data-rich organisation is sitting on the same advantage — most of it just is not reachable yet. The pattern proven here travels: govern the data, ground the AI, let agents do the legwork, and make it answerable in plain language. The domain changes; the approach does not.