From AI Pilots to Answers Grounded in Your Business Context

BUSINESS GENAI TOOLKIT – DOMAIN-READY CONVERSATIONS WITH YOUR DATA

Your teams already want to ask an LLM about procurement spend, inventory on-hand quantities, or last quarter's P&L - and get an answer they can act on. The reason they can't do that yet isn't the model; it's that the model doesn't know exactly what your business terms and numbers mean. Point it straight at your data and it will answer fluently and wrongly. Our Business GenAI Toolkit teaches it your domain first: we bring a starter kit of domain metadata and instruction sets, distilled from cross-company implementations, and refine it together with your team to maximize the chances that the LLM reads your procurement, inventory, sales, and finance data the way your controllers do.

Talk to your metadata before you talk to your data - then the conversations are worth having.

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Key Features

  • A domain starter kit, not a blank page. Pre-built metadata and instruction sets for your core domains - procurement, inventory, sales, finance & more - distilled from cross-company implementations and refined together with your team to match how your business actually defines its terms.
  • Metadata-first by design. We map what your teams genuinely encounter in day-to-day operations and encode the business meaning into a metadata and instruction layer first - so that when the LLM connects to your data, fluent guesses become more grounded answers.
  • Built for real business work. Shaped around controlling, ad-hoc analysis, and emerging AI-agent workflows.
  • Your definitions, applied consistently. What "net sales," "on-hand quantity," or "intercompany elimination" mean in your business is pinned down in the instruction set, so answers stay consistent across people and over time.
  • Return on tokens. The toolkit is designed for value per token - well-scoped questions and an instruction layer that reaches the right answer without burning cycles. No one controls model behaviour completely; the setup tilts the answer's accuracy, and therefore its business value, in your favour.
  • A repeatable way of working. You keep a documented, best-practice setup for doing analytics with LLMs - patterns your team reuses as you extend GenAI to new domains.

PROJECT ACTIVITIES

‍

1

Business ecosystem and question discovery

We start from what your teams actually do - the reports they live in and the questions they keep asking. This is where we identify what "good answers" look like.

2

Domain tailoring from the starter kit

We take the pre-built metadata and instruction set for your chosen domain as the starting point and refine it together with your controllers and domain owners - encoding your business's real definitions, rules, and edge cases until it reflects how your business actually reads its numbers.

3

Asking real questions of your data

Working within the data access your platform and policies already allow, we bring the tailored domain context to your live data and start asking the real questions - the point where grounded conversations begin. How and where that connection is made stays your team's decision, made with your IT and security stakeholders.

4

Review and handover

We run the tailored domain against a question set your team defines, so you can see how it performs on the questions you care about and we tune from there. You keep the setup documented as a repeatable pattern - and the method to extend and evolve it.

DELIVERABLES

Tailored domain context

A working, conversational domain

Documented, repeatable setup

Guidelines to leverage and improve your setup

technology & Ecosystem

Leading LLMs (Anthropic Claude, OpenAI ChatGPT, Google Gemini & more)

A range of GenAI platforms for enterprise deployments – with strong reasoning over structured data, mature enterprise controls, MCP support.

Compatible data platforms

Microsoft Fabric, Databricks, Snowflake, Google BigQuery, Azure Synapse, Azure SQL, Oracle - or any platform with a queryable semantic or data model layer.

Assumptions

  • Customer has an existing, queryable data platform (warehouse, lakehouse, or equivalent) with data models we can work against.
  • How and where the LLM accesses that data is the customer's decision, made with IT and security stakeholders; the customer provides and authorizes the access path
  • Customer provides (or procures) the required LLM subscriptions, API capacity, and cloud resources
  • Controllers and domain owners are available for the tailoring sessions - their input on definitions, rules, and edge cases is what makes the domain context correct
  • Scope is per domain, to be jointly agreed prior to the project

Optional Add-Ons

  • If your platform and access architecture aren't there yet, our GenAI Deployment Blueprint establishes that secure foundation first.
  • Extra business areas
  • Data marts

What’s Next?

Contact us to schedule a discovery call and map the path from scattered AI experiments to a secure, organization-wide foundation.
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