Generative AI development services

What you get

• Retrieval-augmented generation grounded in your content, with citations
• Agents that call your tools and APIs under scoped permissions
• Document intelligence: classify, extract, validate, act
• Guardrails enforced in code rather than in prompt instructions
• Evaluation harnesses you can run on every change
• Cloud, VPC, on-premise or air-gapped deployment

• GPT-4o
• Claude
• LangGraph
• pgvector
• FastAPI

The architecture follows your constraints. We do not add components because they are fashionable, and where a hosted product would do the job we say so before quoting a build.

What generative AI development services actually build

Three shapes cover almost everything worth building. Retrieval systems that answer from your own content with sources attached. Agents that take actions in your systems rather than describing them. And document intelligence that turns unstructured input into structured, validated data.

Most real projects combine two of the three. A clinical orchestrator retrieves records and acts on them. An onboarding pipeline reads documents and updates a system of record. The interesting engineering is almost never the model call; it is what surrounds it.

Grounding is architecture, not instruction

A prompt telling a model not to invent information is not a control. Systems that reliably avoid fabricated claims are built so answers are assembled from retrieved passages with sources attached, and when retrieval surfaces nothing supporting, the system says so rather than generating prose.

On a clinical orchestrator we built, that constraint is why the records agent carries zero citation hallucinations. It is a structural property of how answers are composed, not a hopeful instruction appended to a prompt.

Choosing a model, and why it matters less than you think

Teams spend weeks on model selection and days on retrieval, which is the wrong ratio. If the passage containing the answer never reaches the model, a better model does not help. Measure retrieval in isolation before touching the model, and the problem usually reframes itself.

Where model choice does matter is cost, latency and deployment. Routing simple requests to a smaller model is frequently the largest saving available in a production system. And if data cannot leave your network, an open-weight model behind your firewall decides the architecture before anything else does.

Guardrails for generative AI development services

Anything with money, legal or clinical consequence has to be enforced outside the model, in code it cannot talk its way past. Typed schemas so a malformed tool call is rejected before it executes. Authorisation checked against the requesting user rather than a service account. Hard ceilings on spend and recursion. Allowlists for which tools are reachable in which state.

The model chooses what to attempt; the system decides what is permitted. Input and output filtering for prompt injection and data leakage sit on top of that, not instead of it.

Evaluation, or you are guessing

Generative systems fail plausibly. Nothing crashes, so nothing alerts, and a fluent wrong answer reads exactly like a right one. The only defence is an evaluation set of real inputs with known correct outputs, scored on every change.

We build it before the feature and wire it into continuous integration, with retrieval quality tracked separately from answer quality and cost measured alongside both. A change that improves accuracy and triples spend is a trade, not a win, and you should see both numbers together.

Security, residency and deployment

Generative systems touch everything you own, which makes them a security surface rather than a feature. Entitlement enforced inside the retrieval query, encryption in transit and at rest, secrets outside the codebase, tenant isolation, and audit logs recording which user saw which passage.

Where a hosted model is involved we are explicit about what leaves your boundary and configure retention so prompts are not used for training. Where nothing may leave, we run open-weight models inside your network. We have shipped under HIPAA constraints in healthcare and KYC constraints in financial services.

Generative AI consulting before generative AI development

Most engagements that go badly were scoped badly, and the scoping failure is almost always the same one: a capability was chosen before a problem was. We start with generative AI consulting rather than a build for that reason, and the output is frequently a shorter project than the one you asked about.

The assessment covers four things. Which task in your business is expensive, repetitive and language-shaped, because those three properties together are what makes generative AI services pay rather than merely work. What the data behind that task looks like, in its real state rather than as described. What being wrong costs, which sets the entire architecture. And what the arithmetic says: volume multiplied by time saved against build and running cost.

That fourth step is where we say no more often than any other. If a hosted product covers your use case at a price that survives your volume, buying it is the right answer and we will tell you so. Any software development company that has never recommended a product over its own services is either extraordinarily lucky in its enquiries or is not being straight with you.

Where generative AI capabilities actually earn their cost

Four shapes recur across the work we have delivered, and they share the property that the output is checkable.

**Retrieval and question answering** over a corpus your people currently read manually. Policy, contracts, product documentation, clinical guidance. The AI powered part is composition; the value comes from the retrieval underneath it, which is why this is really a search project with a language layer on top.

**Extraction and structuring**, turning documents into fields a system can act on. Natural language processing is the enabling capability, and the modern generation of models handles layout variation that rule-based NLP never could.

**Drafting**, where a person reviews and edits rather than accepts. Clinical notes, claims summaries, first-draft correspondence. Content generation in this sense is valuable precisely because a human is in the loop, and it is the shape we recommend most readily for a first project.

**Classification and routing**, deciding what something is and where it goes. Unglamorous, high volume, and frequently the strongest return in the portfolio because machine learning has been reliable at this for years and the models simply made it cheaper.

What we steer teams away from on a first build: open-ended generation with no verification step, anything where being plausibly wrong is expensive, and anything whose success is described in terms of engagement rather than a task completed.

Generative AI integration is most of the work

The model is a component. The system around it is the project, and this is the single most consistent surprise for teams commissioning their first build.

A customized generative AI system has to seamlessly integrate with the places your data already lives and the systems that have to act on its output. That means identity and permissions, so retrieval respects who is asking. It means connectors to systems of record, with idempotent writes so a retry never duplicates a transaction. It means an exception path for everything the system will not complete, and an audit trail written from the first commit rather than added after the first incident.

We budget integration as the majority of the schedule because it consistently is. A project that spends its first month tuning prompts and its eighth discovering that the target system rejects writes without a field nobody mentioned has sequenced its risk backwards.

The practical discipline: pull real data out of the target systems in week one, attempt a write into staging in week two, and let what breaks shape the plan.

What we hand over

The code, in your repository. The infrastructure definitions. The evaluation set, which is the artefact that lets your own engineers change the system safely after we leave, and the one most engagements never produce. Documentation of the prompts, the retrieval configuration and the thresholds, with the reasoning behind each choice rather than only the value.

We build systems your team can operate without us. That is a commercial position as much as a technical one: a firm that holds a client through dependency has a worse business than one that gets recommended, and the difference shows in how the handover is structured.

Where you want ongoing involvement, it is usually evaluation and drift monitoring rather than development, because the thing that degrades quietly is model behaviour under changing inputs rather than the code.

What a generative AI development company should tell you

That a hosted product will sometimes do the job. If your corpus is small, non-sensitive and needs no custom retrieval logic, buy the product and spend the budget elsewhere. If your problem is one repetitive step between two clean APIs, use an automation tool.

Custom work earns its cost when permissions decide who sees what, when the logic will not fit someone else's canvas, when data residency rules out a hosted service, or when per-seat pricing has become the dominant line item. Outside those four, we will say so.

How an engagement runs

1. Scope the use case

   What the system must produce, who uses it, and what it must never assert. That last list is the one that shapes the architecture, because everything on it becomes a code path rather than a prompt instruction.

2. Evaluation set

   Real inputs with known correct outputs, agreed with you and held out. Retrieval quality is measured separately from answer quality from this point on, which is what makes every later change measurable.

3. Architecture

   Where the data lives, how it is chunked, where entitlement is enforced, which model runs where. The hosted, hybrid or self-hosted decision is made here against your compliance position rather than a preference.

4. Build and integrate

   Ingestion, retrieval, orchestration and the connections into your systems of record. The integration surface is where the schedule goes, so it is scoped explicitly rather than discovered late.

5. Evaluate and harden

   The suite runs in continuous integration so a regression fails a build rather than reaching a user. Guardrails, cost ceilings and the escalation path are wired in here.

6. Deploy and monitor

   Inside your boundary where required, with per-run tracing of retrieved passages, prompts, tool calls and cost. You own the code and can operate it without us.

Proof, from delivered work

• EdgeCIO · FinTech

  Graph + vector retrieval platform

  +40% query accuracy · +35% answer relevance · <200ms end-to-end latency

  Read the case study →

• OphyCare · Healthcare

  One conversational API over four agents

  4 sub-agents unified · 80% code cut in refactor · 0 citation hallucinations

  Read the case study →

Frequently asked