Applied AI built into the software you already run.
AI and machine learning development and integration for the software you already run: we build AI features and workflows that do a defined job, such as answering from your own documents or processing incoming paperwork, with guardrails, testing and people reviewing what matters.
What is applied AI?
Applied AI means using AI models to perform a defined task inside a real product or business process, such as answering questions from company documents or extracting information from forms. It is judged by how well that task is done, with people reviewing the output where mistakes would matter. That is what separates it from experiments that never reach daily use.
- AI that does one defined task well, inside an existing product or workflow
- Answers grounded in your own content, with sources shown
- Quality measured against real examples before every change ships
- Costs per request visible from the first day
- Best for
- Teams spend hours searching documents or tickets for answers
- Ways to start
- Discovery, defined project or ongoing team
- You see progress
- Working software every 1 to 2 weeks
When this is the right call
- Teams spend hours searching documents or tickets for answers.
- Forms, emails or documents are read and sorted by hand in large volumes.
- Customers expect search, assistance or summaries in your product.
- An AI pilot worked in a demo but never reached daily use.
What changes for the business.
The results we design the work around, agreed with you before we start.
AI that does one defined task well, inside an existing product or workflow
Answers grounded in your own content, with sources shown
Quality measured against real examples before every change ships
Costs per request visible from the first day
Applied AI, end to end.
Every AI feature ships with a way to measure it and a fallback when the model is unsure.
AI features in your product
Assistance, search and summaries built into software customers already use.
Document processing
Reading, classifying and extracting information from forms, contracts and correspondence.
Knowledge search
Answers drawn from your own documents, with sources shown.
AI agents with guardrails
Task-specific automation with limited permissions and human checkpoints.
AI readiness review
Where AI fits, what data it needs, and what to leave to ordinary software.
- Retrieval-augmented generation (RAG)
- LLM integration (OpenAI, Anthropic, Gemini)
- Evaluation sets and accuracy testing
- Permission-aware enterprise search
- Prompt, cost and latency management
- Provider abstraction to avoid lock-in
Controls on every AI feature we ship.
Model output varies. These controls make an AI feature predictable enough to put in front of customers and staff.
Permission-aware retrieval
The model only sees content the user is allowed to see.
Evaluation set
Real questions with known good answers, run on every prompt or model change.
Guardrails
Input and output checks, limited tool permissions and clear refusal behaviour.
Human review
Where an output triggers an action that matters, a person confirms it first.
Provider abstraction
OpenAI, Anthropic or Gemini behind one interface, so the model can change.
Cost and latency tracking
Every request logged with its cost, speed and outcome.
Applied AI we have delivered.
A resource management platform for a growing IT services provider
An AI-powered health tracking app for patients and providers
An AI legal assistant that answers questions and drafts documents
Pick the size of the first step.
Most clients start small and expand once the first stage proves itself.
AI use-case review
A short review of where AI would help in a product or process, what data it needs and what to leave to ordinary software.
Knowledge search or assistant
Answers from your own documents, tickets or records, inside an existing product or internal tool.
Document processing workflow
Incoming forms, emails or documents read, classified and extracted, with a review queue for low-confidence items.
- 1
Choose one task
A defined job where AI clearly helps and success can be measured.
- 2
Prepare the data
Content indexed with permissions, and test examples collected.
- 3
Build with guardrails
Retrieval, prompts, tools and checks behind one service.
- 4
Evaluate
Measured against the test set before release, and after every change.
- 5
Operate
Review queues, feedback, cost and quality tracked in production.
How we take an AI feature from idea to dependable production.
Choosing the job, proving quality, managing cost and risk, and knowing when to stop.
Choosing the first job
The best first AI feature is narrow, frequent and checkable: a task people already do many times a week, where a person can verify the result quickly and where a mistake is cheap to correct. Summarising long records, extracting fields from documents, routing requests and answering questions from your own content are typical. Approving payments, giving regulated advice or acting without review are not first features.
Proving quality before launch
We build an evaluation set before the feature: real examples with accepted answers, including awkward and adversarial cases. The feature must meet an agreed pass rate on that set before anyone outside the team sees it, and every later change to prompts, data or model is tested against it again. Users can flag a poor result in one click, and flagged cases become new test examples.
Managing risk
AI features have risks ordinary software does not, and some are listed in the OWASP Top 10 for LLM Applications: prompt injection, sensitive information disclosure and excessive agency among them. Our controls are designed in from the start: permissions enforced on the data the model can see, outputs checked before they reach a record or a customer, limits on what actions the system can take, and logging that records what was produced and why.
For governance, the NIST AI Risk Management Framework gives a structure many organisations use to govern, map, measure and manage AI risk. Where the EU AI Act applies, obligations depend on the use case and its risk category, and your legal team decides how they apply; we design the records and controls that make compliance practical.
Cost and speed
Every model call costs money and time, both growing with the amount of text sent and returned. We estimate cost per useful result before launch, choose the smallest model that meets the quality bar for each step, send only the context a task needs and cache results that do not change. Cost is tracked from the first week, alongside quality and usage.
Your data and the model provider
Whether data sent to a model provider can be used for training, how long it is retained and where it is processed depends on the provider and the plan you use. We check those terms with you before any real data is sent, prefer business plans with clear data-use commitments, and keep personal data out of prompts where the task allows.
When to stop
Not every AI idea should ship. If the feature cannot reach the pass rate, if users do not accept its output, or if the cost per useful result is higher than the value, the right decision is to stop or change the job. We agree that decision point at the start, so stopping is a planned outcome rather than an awkward conversation.
An example
A support team spends much of its time reading long ticket histories before replying. The first feature summarises each ticket's history and drafts a reply from the help centre, with sources shown and nothing sent without the agent's approval. It launches to one team after meeting its pass rate on two hundred past tickets, and widens once acceptance holds.
Technology we use for this
- OpenAI
- Anthropic
- Gemini
- RAG
- Vector retrieval
- AI orchestration
- Python
- TypeScript
- PostgreSQL
Applied AI: questions we are asked
What is applied AI?
Applied AI means using AI models to perform a defined task inside a real product or business process, such as answering questions from company documents or extracting information from forms. It is judged by how well that task is done, with people reviewing the output where mistakes would matter. That is what separates it from experiments that never reach daily use.
Will our data be used to train the model?
Business API agreements from the major providers generally exclude customer data from training by default. We confirm the terms for the provider you choose and design the system so only the content needed for each request is sent.
How do you stop the AI making things up?
Answers are grounded in retrieved content and show their sources. Evaluation sets catch regressions, and where the stakes are high the output goes to a person before anything happens.
Do we need our own model?
Rarely. Most business tasks are well served by a hosted model with retrieval over your own content. Fine-tuning or self-hosting comes up mainly when data cannot leave a region or costs at scale justify it.
What is an AI agent, practically?
Software that uses a model to decide which of a limited set of tools to call to complete a task, such as looking up an order and drafting a reply. We give agents narrow permissions and human checkpoints.
Solutions and reading.
Get a proposal for applied AI.
Tell us what you need. We reply with a clear next step, usually a short call and a written plan.
- 1We read your brief properly
- 2You speak with people who can discuss the business and the engineering
- 3You get a clear next step: a scoped engagement, a discovery phase or a proposal
Start a conversation
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