AI Engineering & Integration
We engineer AI into the websites and SaaS platforms you already run: assistants, search and automation that answer from your own proprietary data through access-controlled retrieval-augmented generation (RAG). No need to rebuild what you've built.
Your data already has the answers.
Behind this text: each point is a passage of text, placed by meaning. A question pulls in its nearest neighbours. That is retrieval.
01AI Engineering & Integration
Generic assistants answer from their training data and the public web. Yours should answer from your catalogue, your policies, your tickets and your records, inside the product your customers and team already use. That is the work we do.
Track A
Turn the site you already have into something visitors can ask. Every answer comes from your own content and links back to it.
Track B
Put AI where the work happens: inside your product and operations, acting on real records with scoped permissions and a person in the loop where it matters.
The foundation under both tracks
Retrieval-augmented generation gives a model your knowledge at the moment of the question: retrieved, permission-checked and cited. We engineer the whole pipeline, and we measure it.
model-agnostic Works with OpenAI, Anthropic, Google and open-weight models.
Step through the pipeline02How RAG works
In plain terms: it finds the right passages in your material, checks who is allowed to see them, then drafts an answer you can verify.
A language model only knows what it was trained on. RAG hands it the right passages of your knowledge, for this user, at the moment they ask. Step through it, or run the demo.
Step 1 of 7
Docs, databases, tickets, wikis, CRM records. We connect to the systems you already use and keep each record's source and permissions attached from the very first step.
Step 2 of 7
Documents are split into passages sized for retrieval: by headings, sections and meaning, not arbitrary character counts. Good chunking is often the difference between a precise answer and a vague one.
Step 3 of 7
Each chunk becomes a vector: a long list of numbers that places it in a space where similar meaning sits close together. “Refund window” and “money-back period” land near each other even though they share no words.
Step 4 of 7
Vectors and their metadata are indexed for fast similarity search, in a dedicated vector database or inside the Postgres you already run. Clusters form by meaning, not by file or folder.
Step 5 of 7
The question is embedded the same way and its nearest chunks are found. Then the user's permissions are applied: here the closest match is a finance memo this support agent can't open, so it is filtered out before anything reaches the model.
Step 6 of 7
The model receives the question plus only the retrieved passages, with instructions to answer from them and to say so when it can't. The model is a component you can swap, not a foundation you're locked to.
Step 7 of 7
The answer comes back tied to the exact passages it used, so your users can check it, and so can you. When the sources don't cover a question, the right answer is “I don't know”, and the system is built to give it.
Illustrative demo with fictional data. It runs entirely in your browser and makes no network requests.
03MLOps & LLMOps
Providers update models. Content changes. Prompts regress. Costs creep. MLOps (keeping AI reliable after launch) is how we run AI like the production system it is, with the evidence to show it is still working.
The dashboard on this page is an illustration with sample data, not client data.
Golden question sets and regression tests on every prompt, index or model change.
Input and output policies, PII redaction, refusal and escalation paths.
Quality, latency and errors, traced end to end from question to source.
Retrieval and answer quality tracked over time, so slow decay gets caught early.
Caching, right-sized model routing and token budgets.
Prompts, indexes and models versioned, with staged rollout and rollback.
04Data & Analytics
Before AI comes data you can trust. We evaluate the disparate sources you already have, then build the analytics that move you up the ladder: from describing the past, to forecasting, to recommending action.
Descriptive
Clean, joined, trustworthy history. Reporting and dashboards that answer the questions your team actually asks.
Predictive
Forecasts with honest uncertainty: demand, churn, load and risk, with the confidence band shown rather than hidden.
Prescriptive
Recommendations tied to a decision and its trade-offs, so the output is an action someone can take, with the reasoning attached.
Warehouses, spreadsheets, SaaS exports, logs. We assess what you have for quality, joinability and what it can genuinely support, before anyone builds on it.
Built around the decisions they inform, not vanity counts. Fewer charts, clearer owners, numbers that reconcile.
05Custom SaaS
When a feature isn't enough, we build the product, with retrieval, permissions and evaluation designed in from day one rather than bolted on in version three.
A working product in front of real users early, then iterated.
Per-tenant data isolation designed into storage, retrieval and access from the start.
Authentication, billing, admin, audit logs, observability.
06Process
Map the use case, the data behind it, the risks and the metric that defines success.
A working slice on your real data, evaluated against your real questions.
Access control, security review, evals, guardrails, load and cost testing.
Monitoring, drift detection and iteration, or a clean handover to your team.
07Security posture
These are the practices we design in. Every deployment is scoped to your own threat model and compliance requirements.
Retrieval is designed to respect the same permissions as your source systems, so answers are limited to content each user is entitled to see, under the access rules we implement with you.
Sensitive fields detected and redacted before indexing or before prompts, where your data calls for it.
Model provider configurations and agreements that exclude your data from model training, wherever the provider offers that option.
Answer quality measured against your questions, with input and output policies enforced in the pipeline.
Who asked what, which sources were retrieved and which model answered, recorded for review.
Including running inside your own cloud account, with your keys and the retention periods you set, where that's required.
08Start a project
Describe the product, the data it should know, and what “working” would look like. We'll reply by email with questions or a proposed next step.
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