01
LLM applications
Products with a model inside them: the interface, the state, the permissions and the fallback behaviour, not a prompt behind a text box.
Capability / AI engineering
AI engineering, built as product rather than demonstration.
A language model is a component, not an architecture. The work is the pipeline around it: extraction, retrieval, human review, evaluation, and the cost and failure behaviour a production system has to hold.
Coverage
What the discipline covers.
The interesting part of an AI system is rarely the model call. It is what happens to the output, who checks it, what it is allowed to touch, and what it costs to run every day.
02
Document intelligence
Reports, forms and statements read into structured records, with every value traceable back to the page and the passage it came from.
03
Extraction with human review
A reviewer sees what was proposed and what it was read from, then approves or corrects before anything is committed downstream.
04
Retrieval
Indexing, chunking and ranking over your own content, with the access model of the source system preserved in the answer.
05
Agents and orchestration
Tool use with defined boundaries, retries and a record of what ran, so a failed step is visible rather than silent.
06
Evaluation and cost
Test sets, regression when a prompt or a model changes, and inference cost tracked as an operating figure rather than a surprise.
Depth
The kinds of AI work we take on.
From proving whether an approach holds, through to running an evaluated pipeline after launch.
| Type of work | What it involves | How it is engaged |
|---|---|---|
| Proving a use case | A narrow build on your real documents or data, scoped to answer whether the approach holds before anyone commits to a platform. | Project delivery |
| Extraction into a system of record | Parsing, structuring, a review workflow and the write path into the platform that owns the record. | Managed pod |
| Retrieval over internal content | Indexing, ranking and permission-aware answers, evaluated against questions your people actually ask. | Dedicated people |
| Generative media in a product | Generation engines built inside the product rather than licensed in, so the capability stays with the product. | Project delivery |
| Running it after launch | Evaluation suites, drift checks, cost monitoring and model upgrades as providers move underneath you. | Dedicated people, business as usual |
Technology and people
What we work with, and who does the work.
Model providers change faster than the systems built on them, so the pipeline is designed to survive a swap.
Models and serving
- Hosted model APIs
- Open-weight models, self-hosted
- GPU and CPU inference
- In-region endpoints
Retrieval and data
- Vector and hybrid search
- Document parsing and OCR
- Chunking and ranking
- Schema and validation
Application
- Python
- TypeScript and Node
- React review interfaces
- Queues and background workers
Operations
- Evaluation suites
- Prompt and model versioning
- Tracing and logging
- Token and cost monitoring
Roles and seniority available
AI engineer
- Pipelines, retrieval, prompts and evaluation
- Lead, senior and mid-level
- Works against your data, not a sample set
Backend engineer
- Services, queues, storage and the write path
- Senior, mid-level and associate
- Idempotency and failure handling
Front-end engineer
- Review interfaces and product surfaces
- Senior and mid-level
- Accessibility held to your standard
QA engineer
- Output testing and regression on model change
- Mid-level and associate
- Evidence attached to every defect
Inside your team
How the work runs under your constraints.
Most AI work is constrained by something other than the model: where data may sit, who has to approve an output, and what it may cost per month.
Your data stays where you require it
Where a jurisdiction, a client contract or an internal policy fixes where data may be processed, inference is deployed to meet it. Self-hosted models and in-region endpoints are both on the table.
- In-region or self-hosted inference
- Provider terms that exclude training on your content
- Access limited to what the work needs
Confidence is shown, not assumed
Output is presented with its source and its uncertainty. Where a wrong value carries consequence, a person approves before the record is written.
- Source traceability on every field
- Human approval where it matters
- Nothing committed silently
Cost is a design constraint
Model choice, context size and caching are decided against a cost per document or per request that you can forecast before launch.
- Cost measured per unit of work
- Smaller models where they hold up
- Ceilings agreed before build
Proof
Generation engines built and owned, not licensed.
We describe the work, not the client.
What it evidences
Generative capability inside a product
- Image and video advertisement generation engines built in house rather than integrated from a vendor
- Generation delivered as a feature of the product, not an external production step
- Proprietary technology held by us, with no licence dependency on a third party
- Built alongside the integration layer, where decisions in one constrain the other
Full-stack campaign platform built for a United States marketing company, with advertisement generation engines built in house
A campaign platform that runs United States marketing campaigns from one portal and generates image and video advertisements through engines we built and own.
Read the case studyNext step
Bring us the documents and the constraint.
Tell us what has to be read, decided or generated, and where the data is allowed to sit. We will tell you what is realistic.