AI & Machine Learning Development
AI that holds up after the demo
Getting a model to look impressive in a notebook is the easy part. We build the evaluation, guardrails, data pipelines and cost controls that decide whether it still works on your worst day of traffic.
A senior engineer replies within 24 hours — not a sales rep.
Why most AI projects stall between prototype and production
The prototype works. Nobody can say whether it's actually right.
Someone on the team wired up a model over a weekend and the first ten examples looked great. Then it went to twenty users and started confidently returning wrong answers. Without an evaluation set built from your real data, "is it good?" stays a matter of opinion — and opinions don't survive a board meeting.
Your data isn't in the shape the model needs
Labels are inconsistent between teams, half the historical records were entered free-text, and the field everyone assumed was authoritative has been overwritten by two different integrations. Model quality is capped by this long before it's capped by architecture.
The bill scales faster than the value
Inference costs look trivial in testing and alarming at volume, especially when every request pulls a large context window or re-embeds documents that haven't changed. Teams discover this in month three, after the pricing page is already public.
Nobody has decided what happens when it's wrong
An AI feature that touches money, health, hiring or legal text will be wrong sometimes. If there's no review queue, no confidence threshold and no audit trail showing what the system saw and why it answered, your exposure is a support ticket away.
How we build AI systems that earn their keep
We treat an AI feature as an engineering problem with a probabilistic component, not as a magic layer bolted onto your app. That means measurement first, and a willingness to tell you when the answer is no.
Evaluation before implementation
We build a graded test set from your own data — the ordinary cases, the edge cases and the ones your team argues about — and measure against it from the first week. Every prompt change, model swap or retrieval tweak gets scored, so improvement is demonstrated rather than asserted.
Honest feasibility, including the no
Some ideas are better served by a database query, a rules engine or a better form. We'll say so during discovery instead of billing you for six months to find out. Killing a weak AI idea early is one of the cheapest wins available to you.
Guardrails and human review where the stakes justify them
Confidence thresholds that route uncertain cases to a person, input and output validation, structured schemas instead of free text, and logging that lets you reconstruct any decision the system made. Reviewers get a queue designed for speed, not a spreadsheet.
Cost and latency treated as product requirements
Model routing so cheap requests don't pay premium rates, caching for repeated work, batching where latency allows, and smaller fine-tuned models where a large one is overkill. You see the per-request economics before the feature ships, not after.
The stack we build on
Chosen to fit the problem — not because it's what we used last time.
Models & frameworks
- PyTorch
- TensorFlow
- scikit-learn
- Hugging Face Transformers
- LangChain
- LlamaIndex
- spaCy
Data & retrieval
- Postgres with pgvector
- Pinecone
- Weaviate
- Elasticsearch
- Apache Airflow
- dbt
- Snowflake
Serving & MLOps
- FastAPI
- Docker
- Kubernetes
- MLflow
- Ray
- Triton Inference Server
- Weights & Biases
Cloud platforms
- AWS SageMaker
- Google Vertex AI
- Azure Machine Learning
- AWS Bedrock
- Databricks
How we'll work together
Every stage ends with something in your hands — not a status update.
- 01
Feasibility and data audit
We look at the data you actually have, not the data the plan assumes. Volume, label quality, leakage, coverage of the cases that matter — plus a blunt read on whether machine learning is the right tool here at all.
You get: A written feasibility assessment with a go / no-go recommendation, the data gaps that must be closed first, and a rough cost envelope for inference at your expected volume.
- 02
Baseline and evaluation harness
Before any clever modelling, we establish the simplest approach that could work and the scoring set we'll measure everything against. Often the baseline is closer to acceptable than anyone expected, which changes the budget conversation.
You get: A running baseline model, a versioned evaluation dataset drawn from your records, and a scoreboard your team can read without a data science background.
- 03
Iterate against the score
Retrieval strategy, prompt structure, fine-tuning, feature engineering — each change is an experiment with a number attached. We keep the ones that move the metric and discard the ones that just feel better.
You get: A tuned model or pipeline meeting the accuracy threshold agreed in step one, with an experiment log showing what was tried and what it scored.
- 04
Ship with monitoring and a fallback
Deployment includes drift detection, cost dashboards, a review queue for low-confidence output, and a defined path for what the product does when the model is unavailable or plainly wrong.
You get: The production deployment, monitoring dashboards for accuracy and spend, a retraining runbook, and documentation your engineers can operate from.
Where we've applied this
Healthcare
Clinical documentation summarisation and triage support where every output is reviewed by a clinician and the full input-to-answer trail is retained for audit.
Logistics
Demand and ETA forecasting that accounts for seasonality and route disruption, plus document extraction that pulls line items off scanned bills of lading and customs paperwork.
Finance
Anomaly detection on transaction streams tuned for the false-positive rate your compliance team can actually staff, with explanations attached to each flag.
Retail
Recommendation and search ranking that responds to stock reality, so the model stops promoting the product you sold out of yesterday.
Manufacturing
Predictive maintenance on sensor telemetry and visual inspection models that catch defects the line moves too fast for a person to reliably see.
Professional services
Contract and document review assistants that surface the relevant clause and cite where it came from, leaving the judgement call with the fee earner.
Clients we've built for
Real products, in production, with real users on them.
Questions buyers ask us
That's the first question we try to answer, and often the answer is no. If the rule is stable and writable, a rules engine is cheaper, faster and easier to defend. We use discovery to test the idea against your data before you commit to a build, and we'd rather lose a project at that stage than deliver an expensive way to do something simple.
It depends on the task. Classification on your own labelled examples can work with a few thousand well-labelled records; a retrieval assistant over your documents needs the documents to be findable and current more than it needs volume. What kills projects is not quantity but inconsistency — two teams labelling the same thing differently will beat any model architecture. The data audit in step one tells you where you stand.
Three things, roughly in order. Data readiness — cleaning and labelling is frequently the largest line item. The accuracy bar, because moving from decent to reliable takes far more iteration than getting to decent. And ongoing inference spend, which is an operating cost rather than a build cost and needs to be modelled against your usage before you price the feature.
No. Your data stays within your environment and your accounts wherever the architecture allows, and it isn't used for anything outside your project. Where a third-party model provider is involved we'll tell you exactly which one, what leaves your infrastructure, and what the retention terms are — before you sign off on the design.
Yes, and it's a common arrangement. Research teams often have strong modelling work that never reached production because the serving, monitoring and pipeline engineering wasn't their focus. We can take that hand-off and productionise it, or embed engineers alongside your team on shared infrastructure.
It will — user behaviour shifts, your catalogue changes, upstream data formats move. That's why monitoring for drift and accuracy is part of the deployment rather than an afterthought. You get alerting when scores fall below the agreed threshold and a documented retraining procedure. Most clients keep us on a support arrangement for exactly this, though the runbook is written so your team can run it alone.
Tell us what you're trying to predict, extract or automate
Send us the problem and whatever you know about your data. A senior engineer replies within 24 hours with a straight read on whether it's a good fit for machine learning — including if the answer is that it isn't.