AI systems that survive production
Most AI features demo well and fail quietly once real users arrive. We build retrieval, document and automation systems with the evaluation harness attached from the start, so quality is a number you can watch rather than an opinion you argue about.
What you end up with
- Retrieval accuracy measured against a labelled benchmark, not judged by feel
- Hallucination and latency budgets enforced before a change ships
- Automated intelligence over your own documents and knowledge
- Data foundations reliable enough that analytics and AI agree with each other
AI, data & intelligent systems
This is where our engineering depth is concentrated. Our deepest experience is in high-stakes AI integration, evaluation and data platform work — the parts that decide whether an AI feature survives contact with production.
01Retrieval-augmented generation
Chunking that preserves structure, hybrid retrieval, reranking, and grounded answers that cite the source page. Benchmarked against a labelled question set before release.
AI evaluation, reliability & optimisation
Evaluation harnesses for retrieval quality, answer accuracy, hallucination rate and latency — wired into CI so a regression blocks the deploy instead of reaching users.
Intelligent document & knowledge systems
Extraction from messy PDFs and scans, embedding pipelines, semantic search, knowledge graphs and contextual access across structured and unstructured sources.
AI-enabled workflow automation
Models joined to business rules, APIs and databases to automate judgement-heavy steps — with confidence thresholds and human review where the cost of being wrong is high.
Analytics & decision intelligence
KPI frameworks, forecasting, segmentation, scoring engines and funnel analysis that turn warehoused data into decisions someone actually makes.
Data platforms & analytics engineering
Ingestion, dbt and Airflow transformations, analytical modelling, metric standardisation and data-quality assertions that keep the numbers trustworthy.
Tools we reach forChosen per problem, not per trend
- Python
- LangChain
- PyTorch
- Qdrant
- Ragas
- DeepEval
- dbt
- Airflow
- Snowflake
Scoped for you, not picked from a price list
Every project is different, so we do not sell fixed packages. We learn what you need first, then propose the scope, timeline and price that fit, in writing, before any work starts.
From first message
to a written proposal
- 1You tell us what you need, by email, a short call or WhatsApp. An engineer reads it, not a sales team.
- 2We reply with clarifying questions and an honest read on fit. We do not take on every project, and we will say so if we are not the right team.
- 3If it looks like a fit, we talk through scope, requirements, deliverables, timing and budget expectations. Larger or uncertain projects may start with a scoped discovery phase.
- 4Where it makes sense, we send a written proposal with scope, timeline, price and terms. You decide whether to go ahead.
What shapes the scope and cost
- How many workflows, documents or data sources the system has to handle
- The quality and accessibility of the data it depends on
- How accurate it has to be, and how that will be measured before launch
- Where it must run: hosted model APIs or your own infrastructure, and any data-residency rules
- Which existing tools it connects to, and who reviews its output
Useful to have ready
- The task or decision you want AI to help with, and how it is done today
- Examples of the documents, questions or data involved (nothing confidential until an NDA is in place)
- What a good answer looks like, and what a costly mistake looks like
- Any limits on where your data can go
None of this is required to get in touch. If you are not sure yet, that is what the first conversation is for.
Related work and capabilities
We already have an AI prototype that isn't working. Can you fix it rather than restart?
Usually yes, and that is a common way engagements start. We begin by measuring what it currently does — retrieval accuracy, failure modes, latency — because you cannot fix what nobody has quantified. Sometimes the honest answer is that the retrieval design is wrong and a rebuild is cheaper than a repair. We will say so either way.
How do you prove an AI system is accurate enough to launch?
We build a labelled benchmark set from your real questions and documents, then measure retrieval and answer quality against it on every change. You get the numbers, the failure cases, and a threshold agreed with you that a release has to clear.
Do you use our data to train models?
No. Your data is used to serve your system only. If the system calls a third-party AI service, such as a hosted model for transcription or analysis, we name that provider in the proposal before any of your data is sent to it. We can arrange a mutual NDA before sensitive material is shared.
