Compliance-first delivery
Move GenAI from demo to dependable
A generative AI demo can be built in an afternoon. Turning it into something your customers or staff can rely on is a different job: answers must be grounded in current, permitted data; sensitive information must not leak; costs must be predictable; and you need a way to know whether last week’s prompt change made things better or worse.
We engineer GenAI systems end to end. Retrieval pipelines index your documents and systems with access controls respected, so the model answers from your knowledge with citations. Agents get narrowly scoped tools, approval steps for consequential actions and full traces of what they did. Every use case gets an evaluation set and automated checks that run before changes ship, plus guardrails for PII, prompt injection and off-topic requests — the approach outlined in our article on RAG in production with guardrails. The data and serving layer comes from our AI and data engineering practice.
The result is an assistant, agent or copilot with measured quality, observable cost and an audit trail, integrated into the tools people already use. For customer-facing conversational use cases across web and messaging channels, see our chatbot and virtual assistant development; for governance and risk documentation around AI, our security and compliance team can help.
- RAG grounded in your documents and systems, with citations
- Agents with scoped tools and human approval steps
- Evaluation sets and regression checks before every release
- Guardrails for PII, prompt injection and policy
- Tracing of prompts, cost and outcomes
What's Included
Everything you need, delivered by a senior, security-first team.
RAG Assistants
Question answering over policies, manuals and tickets, with permission-aware retrieval and citations.
AI Agents
Multi-step workflows that call your APIs with scoped permissions and human-in-the-loop checkpoints.
Internal Copilots
Assistants embedded in your apps or tools for drafting, summarising and searching.
Guardrails
Input/output filtering, PII redaction, injection defences and policy enforcement.
Evaluation
Golden datasets, automated scoring and red-team tests to catch regressions.
LLM Observability
Traces of prompts, retrieval, latency and spend per user and feature.
From impressive demo to dependable product
A generative AI prototype can be built in an afternoon. Making it accurate, safe, affordable and trusted by real users is the hard part. We design and build large language model (LLM) applications — retrieval-augmented assistants, task-performing agents and AI features inside existing products — with the evaluation, guardrails and observability needed to run them in production.
Where generative AI delivers value
- Knowledge assistants that answer questions from policies, manuals, tickets and contracts, with citations.
- Document processing — extracting fields from forms, invoices, referrals or reports into structured data.
- Agents and workflow automation that call your APIs to look up orders, draft responses or update records, with human approval where it matters.
- Developer copilots grounded in your codebase and internal documentation.
- Customer-facing assistants — see chatbot and virtual assistant development.
RAG, fine-tuning or agents?
| Technique | What it does | Use when | Watch for |
|---|---|---|---|
| Prompting | Instructions and examples in the prompt | Simple, well-defined tasks | Brittle for complex knowledge |
| RAG | Retrieves relevant documents and grounds answers in them | Answers must reflect your current, private knowledge | Retrieval quality and permissions |
| Fine-tuning | Adapts a model’s style or format with training examples | Consistent format, tone or classification at scale | Needs quality training data; does not add fresh knowledge well |
| Agents / tool use | Model plans steps and calls tools or APIs | Multi-step tasks across systems | Needs strict permissions, limits and approvals |
How we build production GenAI
1. Use-case and risk assessment
We define the task, users, success metrics and the cost of a wrong answer. High-risk outputs get human review; low-risk ones can be automated.
2. Data and retrieval
Document ingestion, chunking, embeddings, hybrid search and re-ranking, with source permissions enforced at query time. Our AI and data engineering team builds the pipelines that keep the index current.
3. Evaluation
A test set of real questions with expected answers, automated scoring for groundedness, relevance and correctness, and regression checks whenever prompts, models or data change.
4. Guardrails
Input and output filtering, prompt-injection defences, PII redaction, refusal behaviour for out-of-scope requests, rate limits and audit logs, informed by the OWASP Top 10 for LLM Applications.
5. Observability and cost control
Tracing of every request, token and latency tracking, user feedback capture, caching and model routing so smaller, cheaper models handle simpler requests.
Our article Taking RAG From Prototype to Production goes deeper into evaluation and guardrails.
Models and platforms
We work with hosted models from Anthropic, OpenAI and Google, cloud platforms such as Amazon Bedrock, Azure OpenAI and Vertex AI, and open-weight models where data residency or cost requires self-hosting. Frameworks such as LangChain, LlamaIndex or plain SDK code are chosen for maintainability rather than fashion.
Security and compliance
Data stays in your cloud tenancy where possible, provider settings that prevent training on your data are enabled, and data flows are documented for your security and compliance programme. Applications handling health data are designed with HIPAA requirements in mind.
Common pitfalls
- No evaluation set, so nobody knows whether a change made things better or worse.
- Retrieval that ignores document permissions.
- Agents with broad write access and no approval step.
- Unbounded token costs from long contexts and no caching.
How We Deliver a GenAI Use Case
Use-Case Fit
Pick a workflow with clear value and data; define what a good answer looks like.
Prototype & Evaluate
Build a grounded prototype and an evaluation set; measure before expanding.
Harden
Add guardrails, access control, observability and human review where needed.
Roll Out
Integrate, launch to a pilot group, monitor quality and cost, then scale.
Frequently Asked Questions
RAG is a technique where the application first searches your documents or data for relevant passages and then asks the language model to answer using only those passages, ideally with citations. It keeps answers current and grounded in your own information without retraining a model.
We ground answers in retrieved sources, instruct the model to say when it does not know, show citations, and run automated evaluations for groundedness and correctness on every change. High-risk outputs can require human review.
We use enterprise APIs and cloud platforms whose terms exclude customer data from model training, keep data within your cloud tenancy where possible, and can use self-hosted open-weight models when data must not leave your environment.
We work with models from Anthropic, OpenAI and Google, via direct APIs or Amazon Bedrock, Azure OpenAI and Vertex AI, as well as open-weight models. We select per use case based on quality, latency, cost and data requirements.
Usually as a fixed-price pilot that proves value on one use case with an evaluation set, followed by a production build scoped per phase. Model usage costs are billed to your own provider accounts, and we design to keep them predictable.
Book a free consultation. We help you choose a high-value, manageable first use case and define how success will be measured before building.
Ready to get started with Generative AI & Agents?
Book a free consultation — we'll map a secure, practical path forward.