Applications built on language models
Products with the retrieval, prompting and guardrail work that makes output usable.
Services
AI Development
We build AI powered applications where they make practical sense, and say so when they do not.
AI is worth using when it does something a deterministic system cannot, and worth avoiding when it introduces uncertainty into a process that has to be exact.
We build applications powered by language models, AI agents with tool access and permissions, retrieval systems, internal knowledge tools, document processing, and recommendation systems.
We also tell you when AI is not the right solution. We operate a nutrition scoring engine that deliberately calculates rather than predicts, because a number someone acts on has to be explainable and repeatable.
Problems this solves
You may recognise some of these.
- Staff spend hours reading documents to pull out a handful of fields
- Institutional knowledge is buried in files nobody can search
- Support answers the same question in slightly different words all day
- A manual review step is the bottleneck in an otherwise automated process
- You have been sold an AI feature and cannot tell whether it works
What we do
The work itself
AI agents
Agents that act in your systems through defined tools, with permissions and an audit trail.
Retrieval systems
Search across your own content, so answers are grounded in your documents rather than a model memory.
Document processing
Turning unstructured files into structured data your systems can actually use.
Evaluation
The unglamorous part: measuring whether output is good enough to ship, and noticing when it stops being.
Process
How an engagement runs
Decide whether AI belongs here
Some problems want a model. Many want a rule, a query, or a better form.
Establish what good looks like
An evaluation set before a demo, so quality is measured rather than felt.
Build the narrow version
One task, done reliably, in production.
Instrument it
Logging, cost tracking and quality monitoring, because model behaviour drifts.
Widen only where it holds
Expansion follows evidence.
Technology
What we typically build this on
The technology is chosen around the project. The business problem comes first.
- Python
- TypeScript
- Node.js
- PostgreSQL
- BigQuery
- DigitalOcean
Related work
Software we have built
Nutrition Data and Scoring Infrastructure
Digital Nutrition Intelligence
The shared data and scoring layer underneath our nutrition technology, covering more than 800,000 verified food products.
Read the case study →Nutrition and Meal Planning Application
easyChef Pro
A consumer health and nutrition platform combining meal planning, pantry management, grocery workflows, nutrition data and personalised recommendations.
Read the case study →Questions
Things people ask before starting
- How do you decide whether AI is the right tool?
- By what the output is used for. If a person acts on a number, it usually needs to be calculated and explainable. If the task is summarising, extracting, drafting or searching unstructured content, a model is often the right answer.
- How do you stop an AI feature producing wrong answers?
- By grounding it in your own data, constraining what it is allowed to do, and evaluating it against a fixed set of cases before and after release. No approach removes error entirely, which is why the design also covers what happens when it is wrong.
- Can you add AI to software we already run?
- Usually, and that is often the better first move than building something new. That work is covered under AI integration.
Related services
Often part of the same engagement
Tell us what you are trying to build
Tell us about the business, the problem, what you have today, and what you want to accomplish. If we are not the right team, we will tell you.