Service / AI
Custom AI models, trained for your problem.
Models trained or adapted for one job in your business: classification, extraction, ranking, forecasting or generation, with the data pipeline, evaluation and serving to run them.
[ 01 ]The short answer
What is Custom AI Model Development?
Custom AI model development means building a model for a specific task with your own data, rather than renting a general model's best guess. We choose the smallest approach that clears an agreed quality bar: a classical model, a fine-tuned open model, or a distilled student of a frontier model. You get the training pipeline, the evaluation set, the weights and the serving infrastructure, so the model is an asset you own and can retrain.
What you get
- Trained model weights and model card
- Versioned training and evaluation datasets
- Reproducible training pipeline
- Production inference endpoint with monitoring
- Retraining runbook and drift alerts
[ 02 ]What we build
The pieces, built to last.
- 01
Task-specific models
Classifiers, extractors, rankers and generators trained for one job, where a general model is too slow, too costly or too vague.
- 02
Training data pipelines
Labelling workflows, synthetic data where real data is scarce or sensitive, and versioned datasets you can audit.
- 03
Transfer learning
Start from a strong pre-trained model and adapt it, so useful accuracy arrives in days rather than months of training from scratch.
- 04
Evaluation harnesses
Held-out test sets, error analysis by segment and a scorecard that decides whether a new version ships.
- 05
Serving and scaling
Low-latency endpoints with batching, quantisation and autoscaling, on your cloud or ours, with cost per prediction tracked.
- 06
Retraining loops
Drift monitoring and a repeatable retraining job, so the model improves with your data instead of decaying.
[ 03 ]Where it pays off
Where it earns its keep.
- 01
Support and operations triage
Route, tag and prioritise tickets or claims in milliseconds, so people start on the work that needs judgement.
- 02
Underwriting and risk
Score applications or transactions with a model whose features and errors you can explain to a regulator.
- 03
Search and recommendations
Rank products, documents or candidates by what your users actually choose, not by keyword overlap.
- 04
Visual inspection
Detect defects, damage or fraud in images and video, trained on examples from your own line or claims.
[ 04 ]How we deliver
Four steps, one team, start to finish.
The same people scope it, build it and stay after launch. Timings are typical for a first release; we agree yours after the deep dive.
- 01Week 1
Find the decision
We pick the decision or task worth automating, measure how it is done today and agree what better means in numbers.
- 02Week 1 to 2
Build the eval first
A labelled test set from your own data comes before any prompt or training run, so every change is scored, not argued.
- 03Weeks 2 to 10
Build with guardrails
Models suggest, code decides. Permissions, injection checks, human override and audit logs are part of the first version.
- 04Ongoing
Ship and improve
We launch to real users, watch cost, latency and quality per request, and feed failures back into the eval set.
[ 05 ]Stack
Tools we reach for.
Chosen per project, and we work in yours. Nothing here is a lock-in: you own the code and the accounts.
- PyTorch
- Hugging Face Transformers
- scikit-learn
- LightGBM
- ONNX Runtime
- vLLM
- Ray
- MLflow
- Weights & Biases
- AWS SageMaker
- Modal
[ 07 ]Further reading
Problems we found, and how we would solve them.
- Models3 min read
Fine-tune, retrieve or prompt? A decision guide
ProblemTeams pick fine-tuning, RAG or custom training by reputation rather than by what the problem needs, and spend months on the wrong one.ProposalDecide by what must change: prompts for instructions, retrieval for knowledge that changes or must be cited, fine-tuning for behaviour, format and cost, and custom models only where the task or data is truly unusual; always prove it on an evaluation set.Read the proposal - Cost3 min read
Your AI bill is a routing problem
ProblemAI applications send every request to a frontier model with a long prompt, so cost and latency grow with usage while most requests never needed that capability.ProposalRoute each request to the cheapest model that passes your evaluation for that kind of request, cache shared prompt prefixes, batch what can wait, and distil a small model for the highest-volume task.Read the proposal
[ 08 ]FAQ
Questions buyers ask.
Less than most teams expect when we start from a pre-trained model. A few hundred to a few thousand good examples often clear the bar for classification or extraction. Where data is thin or sensitive, we add synthetic data and measure whether it helps on real examples.
Yes. You own the weights, the training code and the datasets we build, and the model can run in your own cloud account.
Sometimes. For narrow, high-volume tasks a small trained model is usually faster and far cheaper per request. For open-ended reasoning a frontier model often wins. We test both on your evaluation set before recommending one.
[ 09 ]More AI services
- LLM Fine-Tuning and DistillationFrontier quality at a fraction of the cost.
- AI Agent DevelopmentAgents that finish the job, inside the rules.
- AI Workflow AutomationHours of back-office work, done in minutes.
- RAG DevelopmentAnswers from your documents, with citations.
- AI Memory and Knowledge GraphsAI that remembers what your organisation learned.
- MCP Server DevelopmentYour product, usable by every AI assistant.
- Voice AI AgentsEvery call answered, qualified and logged.
- Document AI and Intelligent Document ProcessingPaperwork turned into clean, checked data.
- Private LLM DeploymentAI on your infrastructure, data never leaves.
- LLMOps, Evaluation and GuardrailsKnow your AI works before customers find out.
- Predictive ML and ForecastingDecisions made on what will happen next.
One company at a time