AI agent development services
Assistants, agents, document intelligence and automated workflows — grounded in your own data, evaluated against real cases, and shipped into production.
The pilot impressed everyone. Then it met the edge cases.
A language model demo is easy. What is hard is the part afterwards: grounding answers in your actual documents, handling the case the model gets confidently wrong, and knowing whether last week's prompt change made things better or worse.
Most stalled AI projects did not fail technically. They failed because nobody defined what a correct answer was, so nobody could tell when the system stopped producing one.
Start from the evaluation, not the demo.
We begin by writing down what a good answer looks like on real cases from your business, then build the retrieval and prompting to hit it — so every later change can be measured rather than argued about.
Systems ship with citations back to source documents, a confidence threshold that routes uncertain cases to a person, and logging that shows exactly what the model saw.
What your build can include
- Use-case selection and feasibility assessment
- Retrieval-augmented generation over your own content
- Document extraction, classification and validation
- AI assistants for customers and for internal teams
- Agents that execute multi-step workflows across your tools
- Evaluation sets, regression testing and prompt version control
- Human-in-the-loop review, guardrails and cost controls
Technology, chosen for the job
- Models: Claude, OpenAI, Open-weight models
- Retrieval: Vector search, PostgreSQL + pgvector, Hybrid ranking
- Runtime: Node.js, Streaming APIs, Queues, Evaluation harness
AI chatbot development services, with a human handoff
Use Aria on our homepage to explore the assistant experience. It answers from published company content and helps visitors take the next step; when AI is unavailable, the enquiry form remains available. It is our own implementation, not a client case study.
Our AI automation services can connect approved knowledge, CRM records and document workflows. Discovery defines access rules, provider retention, evaluation cases, actions requiring approval and the handoff path before an agent gets permission to act.
See the engineering in practice
Growth Pilot
Product
Built, owned and operated by Avryxa.
An AI growth workspace: find local businesses that need work done, prove it with an audit, then win and deliver the job.
Open Growth Pilot (opens in a new tab)Avryxa Assistant
Live on this site
Running in production on avryxa.com right now.
The assistant on this site — grounded in the site's own content, so it answers from what we actually publish.
Ask it somethingBefore you start
Where does our data go?
Wherever you require it to go. We work with providers that offer no-training guarantees, and can deploy against a model in your own cloud tenancy where policy demands it. Retention and logging are configured deliberately, not left at defaults.
How do you stop it making things up?
Retrieval grounds answers in your documents and every response cites its sources. Where a question falls outside what was retrieved, the system is built to say so and hand off rather than improvise.
What does a first AI engagement look like?
A narrow, high-volume task with a clear right answer — document extraction, first-line support, internal search. Two to four weeks to a working system evaluated against real cases.
How do you control running costs?
Model routing by task difficulty, caching, and hard budget ceilings with alerting. Cost per resolved case is tracked from the first week.
Can AI features be added to a product we already have?
Yes, and that is often the better path. Adding retrieval and assistance to a product people already use beats launching a separate AI product nobody has a habit around.
Let’s scope your first release.
Tell us your users, main workflow, existing systems and launch priorities. We separate the build, integrations, migration and running costs in a tailored proposal.
Prefer email? contact@avryxa.com