AI that does real work.

We help you find where AI is useful, then build it properly: assistants and search grounded in your own data, document processing and machine-learning models, with testing, cost controls and privacy designed in from the start.

StackLLMsStackRAGStackPythonStackPyTorch

Discuss your project

What we build

AI & machine learning.

Practical AI features — assistants, search over your documents, automation and predictions — built into your product and measured.

Assistants and copilots

Chat and in-app assistants that answer from your own documents and data, and show their sources.

Document and workflow automation

Read invoices, forms and emails, pull out what matters and pass it into your systems.

Search and recommendations

Semantic search and recommendations that help customers and staff find the right thing faster.

Predictive models

Forecasting, classification and anomaly detection trained on your data and monitored in production.

Live demo · runs in your browser

Ask the sample handbook.

This is the first half of a grounded AI assistant: finding the right passages in your own documents. Ask about a fictional parcel company and watch it search, rank, and decide whether it can answer at all.

Search14 passages in 6 documents
RankScore every passage for relevance
CheckDo the best passages cover the question?
AnswerQuote the source, or decline

The sample documents

Delivery promisedelivery-policy.pdfReturns and refundsreturns-policy.pdfDamaged parcelsclaims-guide.pdfChanging a deliverycustomer-help.mdSupport hourssupport-hours.mdData retentionprivacy-notes.md

Answer · grounded in 1 source

Refunds are issued to the original payment method within 5 working days of the returned item reaching our warehouse. [1]

The documents describe a fictional company, and nothing you type leaves your browser. In a real project a language model writes the final answer from these passages, in your tone, and cites them the same way.

The AI pilot

Four weeks to a clear yes or no.

Every AI project starts the same way: one task, your documents, a test set you own, and a decision at the end.

Week 1

Pick and prepare

Agree on one task, gather the documents and write 30 to 50 real test questions with good answers.

Week 2

Build a working prototype

Retrieval over your documents and answers with sources, running in your own cloud account.

Week 3

Measure and harden

Run the test set, fix the failures, and add guardrails for refusals, privacy and cost.

Week 4

Real users, real decision

Put it in front of a small group, review the results, then scale it, change it or stop.

What you keep

Proof, not a demo.

The prototype code, in your repository
A test set you own and can rerun
Measured accuracy and cost per answer
A plan for production, or a clear reason to stop

Plan an AI pilot Read: your first AI feature

What you get

Everything you need to launch — and keep going.

Use-case review and feasibility check
Working prototype on your data
Production AI feature or service
Evaluation tests and quality tracking
Cost, privacy and safety controls
Monitoring and retraining plan

Technology

The stack.

We work in your existing tools where it makes sense.

Models

OpenAIAnthropic ClaudeOpen-source LLMsscikit-learnPyTorch

Retrieval

EmbeddingspgvectorVector databasesLangChain

Data

PythonpandasAirflowData pipelines

Run

FastAPIAmazon BedrockVertex AIEvaluation suites

How We Work

From idea to launch.

Define

We agree goals, priorities and a first release worth shipping.

Workshops and discovery
Architecture review
Scoped plan and prototype

Build

Working software every week, with demos you can try.

Weekly demos
Tested release pipelines
Decisions written down

Operate

We keep it running — or hand it over, fully documented.

Monitoring and alerts
Runbooks and support
Clean handover

FAQ

Questions about AI & ML.

Where should we start with AI?

With one task that costs your team time today. We test it on your real data in a short prototype before you commit to a full build.

Is our data safe?

We design for it: data stays in your accounts where possible, access is limited, and we choose providers and settings that don't train on your data.

How do you know the AI is working?

We build a test set from real examples and check against it with every change, so quality is measured rather than guessed.

Other services

All services