Capabilities
What we do, and how we do it.
ABI AI operates two complementary service lines. Both are delivered by a small, senior team, and both are scoped to what we can genuinely commit to.
Service 01
AI Product Development
We design and build AI systems for organisations that need something specific to their own data, workflow, and constraints — rather than a generic tool bent into shape.
Suited to
- Organisations with a concrete workflow they want to automate or augment
- Teams that need applied AI expertise without hiring a permanent function
- Companies that have run a proof of concept and need it made production-ready
Deliverables
- Custom AI applications built around an existing workflow
- LLM-powered tools, assistants, and agent systems
- AI-enabled workflow automation
- Data and decision-support systems
- Model selection, integration, and evaluation harnesses
- Computer vision and autonomous systems work
- Applied AI research and technical consulting
Operating approach
Start with the decision, not the model
We begin with the decision or workflow the system is meant to improve, and work backwards to what actually needs to be built. Often that is smaller than expected.
Evaluation before scale
A system that cannot be measured cannot be improved or trusted. We build the evaluation alongside the product, not after it.
Built to be handed over
Code, documentation, and evaluation belong to you. We would rather leave a team able to maintain the system than create a dependency.
Service 02
Human Data Operations
Model quality is bounded by data quality. We help AI organisations design, staff, and operate human-data programmes that produce reliable, well-specified data at a defensible standard.
Suited to
- AI labs and model developers building evaluation or preference datasets
- Companies needing domain-expert data in specialist or regulated fields
- Teams scaling an existing annotation effort that has outgrown its process
Deliverables
- Human data collection programme design
- Expert and domain-specific data sourcing
- Annotation guidelines, curation, and quality assurance
- Model evaluation and preference/response data
- Red-teaming and safety evaluation programmes
- Contributor workforce operations and management
- Multilingual and specialist data programmes
Operating approach
Specification is the whole job
Most data quality problems are specification problems. We invest heavily in guidelines, edge-case definitions, and worked examples before volume work begins.
Measure the measurers
Inter-annotator agreement, gold sets, and audit sampling are built into the programme so quality is observed continuously rather than assumed.
Treat contributors as part of the system
Recruitment, calibration, feedback, and retention determine data quality as much as tooling does. We design for the people doing the work.
How an engagement runs
From first conversation to handover.
01
Scoping conversation
A direct discussion of the problem, constraints, timeline, and whether we are the right fit. If we are not, we will say so.
02
Written proposal
Scope, approach, deliverables, and commercial terms in writing, with assumptions and exclusions stated explicitly.
03
Delivery
Work proceeds in reviewable increments with regular checkpoints, so direction can be corrected early rather than at the end.
04
Handover
Documentation, evaluation, and operating procedures transfer to your team, with support arrangements agreed in advance.
Have a project in mind?
Tell us the problem and we will tell you honestly whether we are the right fit.