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Generative AI Development Grounded in Your Own Data

Generative AI development covers the systems that draft, summarise, extract, classify, and translate inside products your team already uses. We build retrieval-augmented assistants grounded in your own documents, extraction pipelines that return validated structured data, and content workflows with a human review step.

  • Answers cite their source paragraph
  • Uncertain extractions route to a human
  • Prompts versioned against an eval set
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How it runs · 5 steps

  1. Find the removable task
  2. Design retrieval
  3. Build the eval set
  4. Validate the output
  5. Ship with citations

Built with

  • Claude
  • GPT
  • Llama
  • pgvector
  • PostgreSQL
  • Hybrid search
  • Pydantic
  • Zod

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Questions first?Talk to an engineer

The most valuable generative AI is rarely the most visible

A pipeline that reads 400 supplier invoices a week and returns validated JSON changes more than a chat interface does — it removes a task rather than adding a tool someone has to remember to open. We start by looking for those, because they are where the return is largest and the risk is smallest.

Where a conversational surface genuinely is the right answer, we ground it in your own documentation, CRM, or product data, and cite back to the source paragraph so a reader can verify rather than trust.

The Problem

What this usually fixes

  • Confident answers with no source

    A fluent paragraph with nothing to check it against teaches users either to over-trust the system or to stop using it. Both kill adoption.

  • Extraction that guesses

    A pipeline that always returns a value, including when it should not, quietly corrupts downstream records with plausible-looking data.

  • Prompt changes that break silently

    A tweak that improves one case regresses twenty others, and nobody finds out until a user reports it weeks later.

  • Knowledge that goes stale

    The index was built once. The documents moved on, and the assistant is now confidently quoting last year's policy.

How We Work

The process

Each step produces something you can review — a document, an environment, or working software — rather than a percentage in a status report.

  1. Find the removable task

    We look for the repetitive, high-volume work first — the pipeline that deletes an hour a day beats the chat window that adds a tab.

  2. Design retrieval

    Chunking, embedding, and refresh strategy for your real documents, with access control applied at query time.

  3. Build the eval set

    Real examples with known correct answers, agreed before implementation, so prompt changes are tested rather than argued about.

  4. Validate the output

    Schema-validated structured returns with confidence thresholds, routing anything uncertain to a human queue instead of guessing.

  5. Ship with citations

    Answers link back to the source paragraph, so verification is a click rather than an act of faith.

What You Get

Why teams choose this

  • Verifiable answers

    Citations to the source paragraph mean a reader can check the claim, which is what makes the system usable for consequential work.

  • Clean structured data

    Schema validation and confidence thresholds keep uncertain results out of your database and in a human queue.

  • Changes you can test

    Versioned prompts run against a fixed eval set, so an improvement in one case cannot silently regress twenty others.

  • Knowledge that stays current

    A defined refresh strategy rather than a one-off index, so the assistant is not quoting a superseded document.

Stack

Technologies we build with

Tools we have delivered production work on, not a capability matrix. We pick per project and will explain the trade-off behind each choice.

Models
  • Claude
  • GPT
  • Llama
Retrieval
  • pgvector
  • PostgreSQL
  • Hybrid search
Validation
  • Pydantic
  • Zod
  • JSON Schema
Evaluation
  • Eval suites
  • Prompt versioning
  • Regression runs
Industries

Who this is for

Strongest fit wherever unstructured text arrives in volume and someone currently reads all of it.

  • Financial services
  • Insurance
  • Legal & professional services
  • Healthcare
  • Logistics
  • E-commerce & retail
  • Education
  • Real estate
Questions

Generative AI Development: common questions

Related reading

What is retrieval-augmented generation, and do we need it?

Retrieval-augmented generation grounds a model's answer in documents fetched from your own systems at query time, rather than relying on what the model absorbed during training. You need it whenever answers must reflect your current, private, or frequently changing information — policies, product data, contracts, support history. You do not need it for tasks that are purely generative, such as rewriting a paragraph or classifying sentiment, where there is no external fact to ground against.

How do you stop the model from making things up?

Three layers, because no single one is sufficient. Retrieval constrains the model to your actual documents rather than its training data. Citations to the source paragraph make an unsupported claim visible to the reader immediately. And structured outputs are schema-validated with confidence thresholds, so an uncertain extraction goes to a human queue instead of into your database. Hallucination is reduced by architecture and caught by validation — instructing the model not to invent things is the weakest of the three.

Can generative AI work with our private documents without exposing them?

Yes. Three deployment options cover almost every constraint: frontier APIs under a zero-retention agreement, open-weight models running inside your own cloud account, or fully on-premise deployment where data may not leave your infrastructure at all. The choice is made against your data-residency and contractual obligations during scoping, not assumed. Retrieval also applies your existing access rules at query time, so the model never sees a document the requesting user could not open.

How do you measure whether a generative AI system is accurate enough?

With an evaluation set built from your real examples and agreed before the build starts. Each example has a known correct answer, and a pass threshold is set against the accuracy the use case actually requires — extracting invoice totals demands a different bar from drafting a first-pass email. The set is re-run on every prompt, retrieval, or model change, which turns "did that make it better" from an opinion into a number.
Related

Services that pair with this

What does your team read every week?

Invoices, tickets, contracts, applications — if people are reading them in volume, there is usually a pipeline worth building.

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