Service
AI Development
Integrate and build intelligent systems, from LLM-powered features and computer vision to predictive analytics and automation.
- LLMs
- Computer Vision
- ML Ops
- Automation
What is AI development?
AI development means adding machine-learning features to software so it can read, classify, summarise or answer questions about your own information. In practice that usually means large language models working on your documents and records, with clear limits on what they are allowed to decide.
The useful applications are narrower and less dramatic than the marketing suggests, and they are mostly about unstructured information: text, documents, forms and messages that a person currently has to read. The engineering problem is rarely the model. It is getting the right information in front of it, keeping the answers grounded in your data, measuring whether the output is good enough, and deciding where a human has to approve the result.
What ABM provides
- Identifying where AI is useful, and where it is not
- LLM features: assistants, search, summarisation
- Document and data extraction
- Workflow automation with human review
- Evaluation, guardrails and cost control
- Integration into your existing product
What can be built
- Assistants that answer questions from your own documents, with sources shown
- Document and form data extraction into structured records
- Classification and routing of incoming email or tickets
- Summarising long reports, calls or case notes
- Search that understands meaning rather than matching keywords
- Drafting tools where a person reviews before anything is sent
- Computer vision for images where the task is well defined
Who AI development is for
- Teams buried in documentsInvoices, forms, contracts, applications and reports that someone reads and retypes.
- Support and service teamsThe same questions answered repeatedly, where the answers already exist somewhere.
- Businesses with data nobody can queryYears of records that only a specialist can get answers out of.
- Product teamsAdding search, assistance or summarising to a product you already run.
How AI development works at ABM
- 01Find the real taskWe look for a step where people read or retype information, and check whether AI is the right tool at all. Sometimes it is not.
- 02Prepare the dataYour documents and records are collected, cleaned and indexed so answers can be grounded in them.
- 03Build the featureThe model is wired into the workflow, with the retrieval, prompts and structure the task needs.
- 04EvaluateOutput is checked against a set of real examples with known answers, so quality is measured rather than assumed.
- 05Add guardrailsLimits on what it can act on, a human approval step where it matters, and a record of what it produced.
- 06Run itCost monitoring, quality monitoring, and updates as the underlying models change.
Technologies and integrations
The exact stack is chosen per project and agreed with you in writing before development starts. These are the tools and categories we work with for AI development.
Models
- Large language models (LLMs)
- Computer vision models
Patterns
- Retrieval over your own documents
- Structured extraction
- Classification
- Summarisation
Engineering
- Evaluation sets
- Guardrails and human review
- Cost and usage monitoring
- ML Ops
Integration
- APIs into your existing product
- Workflow automation
- Queues and background jobs
When to choose AI development
- People spend significant time reading documents and copying information out of them.
- The knowledge needed to answer a question already exists in your files, but nobody can find it.
- The task tolerates review: a person can check the output before it counts.
- You can describe what a good answer looks like, which means quality can be measured.
When something else fits better
- Software DevelopmentThe rules are clear and deterministic. Ordinary code is cheaper, faster and easier to audit than a model.
- API Development & IntegrationThe information is already structured and the problem is simply moving it between systems.
- Rapid MVP DevelopmentYou want to test whether an AI feature is useful before building it into your product.
Where AI development usually fits
Related services
Questions
AI Development: common questions
What does AI development actually include?
For most businesses it means a feature inside existing software: an assistant that answers from your documents, extraction that turns forms into records, classification that routes incoming work, or summarising that shortens something long. The build includes preparing your data, connecting the model, evaluating the output against known-good examples, and deciding where a person must approve the result.
Will it make things up?
Language models can produce confident wrong answers. The mitigations are grounding answers in retrieved source material, showing which document an answer came from, constraining output to a defined structure, and keeping a human approval step wherever a mistake would be costly. We design for that rather than assuming it away.
What happens to our data?
Which model runs where, and what is sent to a third-party provider, is a decision made with you before anything is built, and it is written into the scope. It depends on the sensitivity of your data and on any rules you have to follow.
How do we know whether it is good enough?
By testing it on a set of real examples where the correct answer is already known, and agreeing what accuracy is acceptable before launch. Without that, "it seems to work" is the only available measure, which is not enough to build a process on.
What does it cost to run?
AI features have an ongoing usage cost as well as a build cost, which varies with how much text is processed. That is estimated during scoping and monitored once it is live, so it does not become a surprise.
Thinking about AI development?
Send a short brief. We'll reply within one business day with a next step, or honest advice if we're not the right fit.