AI/ML& Data
What’s Included:
- Predictive analytics
- LLM & RAG integration
- Document extraction
- BI dashboards & pipelines
We build AI features that answer a question somebody is already paying to answer by hand — not demos that impress once and then sit unused.
Working Process
- Find The Decision – We start from a decision your team makes repeatedly and expensively. If we cannot name it, the model has nothing to be judged against.
- Baseline, Then Model – A dumb baseline first, so there is an honest number to beat. Then the smallest model that beats it, evaluated on your data rather than a benchmark.
- Ship & Monitor – Into the product behind a feature flag, with drift monitoring and a fallback path, plus 6–12 months of support.
The Data Work Is The Project
Most AI engagements fail on plumbing, not on modelling. We treat ingestion, labelling, evaluation and monitoring as the deliverable, with the model as one replaceable component inside it.
- Whether you want forecasting, document extraction or an assistant over your own knowledge base, the work starts with getting the data reliable and measurable.
- We cover every stage — pipelines and warehousing, retrieval and embeddings, evaluation harnesses, guardrails, and the dashboards that show whether it is still working.
What We Deliver
- Predictive analytics and forecasting models
- LLM integration with retrieval over your own data
- Computer vision and document extraction
- Data pipelines, warehousing and BI dashboards
Evaluated, Not Just Demonstrated
Every model ships with the evaluation set it was measured on and the score it achieved, so your team can tell later whether a change helped. Notebooks, prompts and pipelines live in your repositories.
Tools & Tech
- Python
- PyTorch
- PostgreSQL
- AWS
Platforms, storefronts and internal systems delivered for clients across eCommerce, health tech, fintech and logistics.
Client satisfaction across delivered engagements — measured on what shipped, not on what was promised at kickoff.
Typical uptime after migration, with monitoring, alerting and zero-downtime deploys set up as part of the build.
What You Get
One senior team from discovery through to launch and beyond — weekly written updates, an open backlog, and direct access to the engineers doing the work. The people in your kickoff call are the people writing the code.
- Data foundation – Ingestion, cleaning and warehousing, so the same numbers appear in every report.
- Model & evaluation – The smallest model that beats a documented baseline, with the harness that proves it.
- Handover & support – Pipelines, prompts and monitoring you own, plus 6–12 months of post-launch support.
How a project runs
The same five stages every time. Pick a project to see what each one produced.
Illustrative engagements — the stages are ours, the names and figures are examples.
- Span
- 18 weeks
- Team
- 4 engineers
- Stack
- Next.js
- Node.js
- PostgreSQL
- Docker
Discovery
2 weeksUnderstand the work before designing for it
Two weeks in the depot and the finance office. Eleven spreadsheets found, four of which disagreed about the same stock.
Signed scope
Architecture
2 weeksMake the expensive decisions deliberately
Stock, jobs and invoices modelled as one ledger, so a movement is recorded once and read everywhere rather than re-keyed three times.
Data model
Build
11 weeksTwo-week cycles against a signed scope
Five cycles. The depot module shipped first because that was the one costing a day a week.
Staging every fortnight
Launch
1 weekA rehearsed go-live with a way back
Run in parallel with the spreadsheets for nine days, until both sets of numbers agreed on a Friday close.
Cutover
Support
6 monthsMonths, not a handover email
Monitoring, month-end patches, and two rounds of changes the depot asked for once they had actually used it.
Handover
[ Project Brief ]
Bring us the brief, webring back the scope
Tell us what the system has to do, the metric you'd move, andthe deadline. Within 4 business hours we send back a writtenscope, a fixed estimate, and the two projects closest to theproblem you're describing.
Rather say it than type it?
Both of these reach the engineers who would do the work — no account manager in between, no discovery deck before the first question.
- eCommerce & Retail
- Health Tech
- Manufacturing
- Logistics
- Hospitality
- Education
- Fintech
- Real Estate
- SaaS & B2B
The engineers who will actually build your system
[ OUR TEAM ]
