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AI Core Development

The AI capability underneath your product, built properly

This is where the intelligence itself gets built — the model, the language layer, the vision pipeline, the voice stack. We design that core so the features you hang off it later have something dependable to stand on.

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A senior engineer replies within 24 hours — not a sales rep.

Where AI builds go wrong at the foundation

A model was chosen before the problem was defined

Somebody picked an architecture, a vendor or an open-weights checkpoint in week one because it was in the news. Six weeks later the constraint that actually mattered — latency budget, on-premise requirement, licence terms for commercial use — makes that choice untenable, and the rebuild costs more than the original build.

It behaves differently every time you ask

Language models are non-deterministic by design. Without structured output schemas, fixed decoding settings and regression tests over a frozen set of inputs, a prompt edit that improves one case quietly breaks four others. Teams find out when a customer screenshots the bad answer.

Nobody owns the thing between the model and the app

Your frontend team assumes the AI layer handles retries, rate limits, token accounting and fallbacks. The data scientist assumes the platform team does. In practice nothing does, and the first provider outage takes down a feature that had no degraded mode.

The proof-of-concept has no path to production

It runs in a notebook against a hand-picked sample, with API keys in plain text and a person restarting it when it hangs. Turning that into something that survives real users, real load and a security review is usually a larger job than the prototype was — and it rarely gets budgeted.

How we build the core

The core layer earns its keep by being boring: predictable interfaces, measurable quality, and no surprises when a provider changes something upstream. We build for the second year, not the launch demo.

Model choice comes after the constraints are written down

Latency ceiling, privacy boundary, deployment target, acceptable error rate, licence conditions, budget per thousand requests. We record those first, then select — hosted API, open-weights model on your own infrastructure, a fine-tune, or classical machine learning where that is genuinely the better fit.

Structured contracts around a probabilistic component

The AI core exposes a typed interface to the rest of your system: defined schemas in and out, validation on both sides, explicit error states. Your product engineers integrate against a contract rather than against a prompt, which means they can build without becoming prompt specialists.

One evaluation harness the whole team can read

Every capability we build ships with a scored test set drawn from your own material and a regression run that fires on each change. When someone proposes a different model next quarter, answering "is it better?" takes an afternoon instead of a debate.

Degraded modes designed in, not discovered

Provider timeouts, rate limits, context-length overruns and outright refusals all get a defined behaviour: retry with backoff, route to a smaller model, fall back to deterministic logic, or fail visibly to a human. The product keeps working when the clever part does not.

What this covers

Pick the piece you need, or bring us the problem and we'll tell you which applies.

AI & ML Development

Custom model work — classification, forecasting, ranking, extraction — where an off-the-shelf API cannot reach the accuracy or the privacy boundary you need. This is our deepest AI service and it has a page of its own covering evaluation, MLOps and cost control in detail.

Supervised models trained on your own labelled records
Fine-tuning and distillation where a smaller model beats a large one on cost
Evaluation harnesses and drift monitoring after launch
Full detail on the dedicated AI & ML Development page

LLM App Development

Applications where a language model does the reasoning — drafting, summarising, extracting, classifying free text, answering from your own content. The engineering is less about the model and more about controlling it: schemas, context assembly, and knowing what to do with an answer you cannot trust.

Structured output with schema validation and repair on malformed responses
Context assembly and token budgeting so long inputs do not silently truncate
Prompt versioning with regression scoring on every change
Model routing between fast, cheap and high-capability tiers per request type

AI Voice Agents

Agents that hold a spoken conversation over the phone or in-app — inbound triage, appointment booking, order status, outbound reminders. Voice is a harder problem than chat because latency is audible: a pause that reads as thoughtful in text reads as broken on a call.

Sub-second turn-taking with barge-in so callers can interrupt naturally
Telephony integration through SIP or a provider like Twilio, with call recording and consent handling
Warm transfer to a human when confidence drops or the caller asks
Accent and background-noise testing against recordings from your actual call queue

Computer Vision

Systems that read images and video — defect detection, object counting, document and label recognition, safety monitoring. Most vision projects succeed or fail on the dataset rather than the architecture, so we start by looking hard at what you can actually capture.

Annotation strategy and dataset review before any training begins
Detection, segmentation and OCR pipelines using PyTorch, YOLO or Detectron2
Edge deployment on constrained hardware where bandwidth or privacy rules out cloud inference
Threshold tuning against the false-positive rate your operators can realistically handle

AI Strategy Consulting

For teams with a list of AI ideas and no reliable way to rank them. We assess each candidate against your data reality, engineering capacity and risk appetite, then say which ones are worth funding — and which are a database query wearing a costume.

Use-case scoring on value, data readiness and delivery difficulty
Build, buy or wait recommendations with the reasoning made explicit
Data and platform gap analysis covering what must exist before any build starts
A sequenced roadmap with owners, and a governance model for approving AI features

The stack we build on

Chosen to fit the problem — not because it's what we used last time.

Language & generative

Claude
GPT-4 class models
Llama
Mistral
Hugging Face Transformers
LangChain
vLLM

Vision & speech

PyTorch
OpenCV
YOLO
Detectron2
Whisper
Twilio
LiveKit

Training & evaluation

scikit-learn
MLflow
Weights & Biases
Ray
LoRA fine-tuning
ONNX Runtime

Serving & infrastructure

FastAPI
Docker
Kubernetes
AWS Bedrock
Google Vertex AI
Azure OpenAI Service
NVIDIA Triton

How we'll work together

Every stage ends with something in your hands — not a status update.

01
Constraint capture
We interrogate the non-negotiables before anything is chosen: where the data may travel, what response time users will tolerate, what an error costs you, and which regulator has an opinion. Most architecture arguments dissolve once these are on paper.
You get: A written constraint sheet, a shortlist of two or three viable model approaches with their trade-offs, and an indicative cost per request for each.
02
Spike the risky part first
We build the single hardest slice — the ambiguous extraction, the noisy audio, the class the vision model keeps confusing — and score it. If the core capability is not achievable at the quality you need, you learn that in weeks rather than after a full build.
You get: A working spike against your real data, a scored result on the hardest cases, and a clear recommendation to continue, adjust scope or stop.
03
Harden into a service
The spike becomes a versioned service with typed inputs and outputs, authentication, rate limiting, cost accounting per caller and full request logging. Your application teams get documentation and a staging endpoint they can build against immediately.
You get: A deployed AI service with API documentation, a staging environment, and the regression suite wired into your build pipeline.
04
Instrument, then tune
Once real traffic arrives we watch quality, latency and spend together, because improving one usually costs one of the others. Tuning decisions get made against observed usage patterns rather than assumptions made during design.
You get: Dashboards covering accuracy, latency and cost, an alerting configuration, and a tuning log recording each change and its measured effect.

Where we've applied this

Healthcare

Speech-to-text and summarisation over clinical encounters, built so the transcript, the prompt and the model version behind every generated note remain auditable.

Manufacturing

Vision models for surface defect and assembly verification, trained on your own line footage and deployed at the edge where sending video to the cloud is impractical.

Finance

Document understanding across statements, KYC packs and loan files, with confidence scores that decide what reaches an analyst and what clears automatically.

Retail

Product enrichment and visual search over catalogue imagery, plus generated descriptions that stay inside the tone and compliance rules your brand team sets.

Logistics

Voice and vision at the depot — spoken check-in for drivers whose hands are full, and container or label recognition from cameras already mounted on the yard.

Education

Assessment support and content generation grounded in an approved syllabus, with citations back to source material so an instructor can verify before it reaches a learner.

Clients we've built for

Real products, in production, with real users on them.

OrbiSim

Science and Mind

Beat22

Prime Selection

Questions buyers ask us

Should we use a hosted model API or run our own?

Start hosted unless something forces you not to. The usual forcing conditions are a hard data-residency rule, a per-request cost that stops making sense at your volume, or a latency floor an external call cannot meet. Self-hosting open-weights models is entirely workable, but it moves GPU capacity planning and model upgrades onto your plate, and that ongoing cost belongs in the comparison.

What does it cost to build an AI core?

The build is driven by how novel the capability is and how much data preparation stands in front of it. A language-model feature over content you already hold is a different order of work from training a vision model on footage you have not captured yet. Running cost is separate and usually more interesting long term — we model spend per request during design so you can price your own feature honestly.

How long until we have something real?

A scored spike on the hardest part of the problem typically takes a few weeks. A hardened, documented service that your product teams can build against is a few months from a standing start. Vision work with no existing labelled dataset runs longer, because annotation is real effort and rushing it produces a model that fails on exactly the cases you built it for.

Who owns the models, prompts and code?

You do, completely, from the first commit. That covers the repository, trained weights from anything we fine-tune on your data, the prompt library, the evaluation sets and the deployment configuration. Where a third-party model provider sits in the architecture we tell you which one, what leaves your infrastructure and under what terms, before you approve the design.

Can you work with our existing engineering team?

Yes, and it is the arrangement we prefer for core work. Your engineers know the domain and the codebase; we bring the AI-specific parts and hand them over deliberately rather than leaving a black box behind. We can also embed alongside an in-house data science team that has good modelling work stuck short of production.

What happens when the underlying model is deprecated?

It will be — provider model lifecycles are short. Because the core sits behind a typed interface and has an evaluation suite attached, swapping the model is a scored experiment rather than a rewrite. We run the new candidate against the same test set, compare quality, latency and cost, and migrate only when the numbers support it. Clients on a support arrangement have us handle that cycle; the rest get a runbook for doing it themselves.

Tell us what the AI actually has to do

Describe the capability and whatever you know about the data behind it. A senior engineer replies within 24 hours with a straight read on the model approach, the likely constraints, and whether the hardest part is achievable at the quality you need.

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