A model moving from data to production
Capabilities_Matrix
Precision engineering for ai & machine learning demands a specialized toolkit.
Foundation Model Integration
Connect your systems to Claude, GPT-4, Gemini, and open-source models via unified, production-hardened APIs.
Custom Model Training
Train domain-specific models on your proprietary data for maximum accuracy, privacy, and cost efficiency.
RAG Architecture
Build retrieval-augmented generation systems that answer questions accurately over your private knowledge base.
MLOps & Monitoring
Automate training, versioning, deployment, and drift monitoring of models in production environments.
Deployment Protocol
Custom models, LLM integrations, RAG pipelines, and MLOps infrastructure built for production scale. We take AI from prototype to product.
Operational Tech_Stack
How the work is sequenced
Evaluation first, model second.
Most machine learning programmes fail in the same order: a model is chosen before anyone agrees what good looks like, and the argument about whether it works arrives after the money is spent. We invert that. The measurement comes first, and it is built from your cases, not a public benchmark.
- 01
Define the decision
Before any modelling, we establish what decision the model informs, who is accountable for it today, and what it costs when it is wrong in each direction. A false positive and a false negative are rarely worth the same, and a model tuned as if they were will be tuned incorrectly.
You end up with
A written decision scope with the cost of each error type.
- 02
Build the evaluation set
We assemble a set of real, labelled cases from your own history — including the hard and ambiguous ones teams usually exclude. This becomes the contract for what shipping means, and it is agreed before work starts so the threshold cannot move to fit the result.
You end up with
A held-out evaluation set and agreed accuracy, latency and cost thresholds.
- 03
Baseline before bespoke
The first candidate is usually a foundation model with retrieval over your documents, or a simple statistical baseline. Both are cheap and both are often sufficient. A custom or fine-tuned model is justified only when the evaluation shows it clears the baseline by enough to pay for its lifetime cost.
You end up with
A measured comparison of baseline and candidate approaches.
- 04
Engineer the serving path
This is where prototypes die: no serving path, no rollback, no version history, no owner. We build the path to production as part of the work rather than after it — versioned artefacts, a staged rollout, and a tested way back that someone has actually exercised.
You end up with
A deployed model with versioning, staged rollout and a rehearsed rollback.
- 05
Instrument for decay
Inputs drift, upstream schemas change, and quality degrades quietly. Monitoring covers input distribution as well as output quality, with thresholds that raise an alert rather than waiting for a downstream team to notice something is off months later.
You end up with
Drift and quality monitoring, with alerting thresholds and an owner.
- 06
Hand over the capability
Retraining, evaluation and release should not require us. We pair with your engineers throughout and document the decisions, not just the code, so the reasoning survives the people who were in the room.
You end up with
Runbooks, retraining procedure and a team that has run it once.
Where it pays for itself
The work that justifies the infrastructure.
Machine learning earns its keep where a judgement is made many times a day, the inputs are already captured, and the cost of being slightly wrong is bounded. These are the patterns that meet that test most often.
Document understanding
Extracting structured, cited fields from contracts, claims, invoices and correspondence, so a reviewer verifies against the source rather than trusting a summary.
Tells you it is real
Staff retype information from one system into another.
Triage and routing
Classifying incoming work by type, urgency and required skill, so the queue arrives sorted instead of being sorted by whoever opens it first.
Tells you it is real
Skilled people spend their first hour deciding what to work on.
Forecasting
Demand, capacity and cash forecasting that accounts for promotions, seasonality and substitution rather than treating each series independently.
Tells you it is real
Plans are built on a spreadsheet nobody fully trusts but everyone uses.
Anomaly detection
Surfacing the transactions, sensor readings or access patterns that do not fit, with enough context attached for an investigator to act without starting from scratch.
Tells you it is real
Problems are found by customers before they are found internally.
Ranking and prioritisation
Ordering leads, cases, inspections or interventions by expected value, so limited capacity goes where it returns most.
Tells you it is real
Capacity is allocated first-come-first-served because nothing better exists.
Quality inspection
Detecting defects against labelled history, run in shadow mode against the live line before it influences any decision.
Tells you it is real
Inspection is a sampled manual process with inconsistent standards.
Where we stop
What we will not build.
Some of these are regulatory, some are engineering judgement, and some are simply that the thing being asked for does not work. Saying so early is cheaper for both of us than discovering it at acceptance.
Models that cannot be explained where an explanation is owed
If a decision can be adversely actioned against a person, it needs a reason a human can defend. We design that path into the decision rather than reconstructing it afterwards, and we decline the work if the accuracy gain depends on removing it.
Training on data you do not have the rights to
Licensing and consent are established before any data reaches a training pipeline. Where the provenance of a dataset cannot be established, it does not get used.
Accuracy claims without a held-out set
A number measured on data the model has seen is not a result. We report against the agreed evaluation set, including the cases where the model does badly.
Automation of a process nobody understands
Automating an undocumented process encodes its accidents permanently. Where the current process cannot be described, mapping it comes first — often it turns out the automation is not the valuable part.
What makes this hard
The demo worked. Production is where models go to die.
Prototypes that never ship
A notebook that scores well offline has no serving path, no rollback and no owner. It stalls at the last 20%.
Silent accuracy decay
Inputs drift, upstream schemas change, and quality degrades for months before anyone downstream notices.
Unbounded inference cost
Token and GPU spend scales with adoption. Without measurement, success becomes the budget problem.
No evidence trail
When a model influences a decision someone can contest, you need to show the inputs, version and reasoning.
Common questions
Before you
get in touch.
Do we need our own data to start?
No. We often begin with a foundation model and retrieval over your existing documents, then move to fine-tuning or a custom model only where the evaluation shows it earns its cost.
How do you measure whether a model is good enough?
We build an evaluation set from your real cases before any model work begins, and agree the accuracy, latency and cost thresholds that define done. Nothing ships against a vibe check.
Can models run inside our own environment?
Yes. We deploy in your cloud account or on-premises where residency, procurement or regulation requires it, including fully open-weight models with no external API calls.
What happens when the model degrades?
Drift and quality monitoring ship with the model, not after it. Alerts are wired to thresholds you approved, and every version is reversible.
How long before we see something working?
A first evaluated prototype against your data typically takes weeks, not quarters. Production hardening depends on the integration and controls your environment needs.
Ready to integrate?
Begin your transformation with our specialized ai & machine learning engineering team.