AI products
Agents · RAG · Assistants · Document AI
We build AI products — agents, retrieval systems, assistants, and document pipelines — as products, with evaluation and guardrails behind them. Standalone, or added to software you already run.
Retrieval before generation
A wrong answer from an AI system is almost always a retrieval failure wearing a language model's voice. We treat the two as separate problems with separate test sets, because tuning a prompt to paper over a retrieval gap produces a system that fails again the moment the corpus changes. Chunking, embedding choice, hybrid keyword-plus-vector search, and reranking are decided against your documents rather than against a blog post's defaults.
Citations, or a refusal
Every answer carries the passages it was drawn from, one click from the source. When retrieval scores below threshold the system says it does not know and offers a handoff, rather than generating something plausible and confident. A system that refuses honestly gets trusted and used; one that bluffs gets abandoned within a month of launch, regardless of how well it demoed.
Evaluation that gates the deploy
Every AI feature ships with a test set built from your real cases — historical tickets, real documents, real questions. Accuracy, refusal rate, and citation coverage run as a regression suite before every release, so a prompt tweak or model upgrade that quietly degrades answers is caught before a customer notices it. Without this, nobody can tell whether a change helped.
Cost and latency as requirements
Cost per request and p95 latency get written down at the start, alongside the functional requirements. Model choice then follows the budget, instead of the budget discovering the model choice after the first invoice. Smaller models handle the routine path, larger ones handle what genuinely needs them, and the routing between them is measured rather than assumed.
A person approves anything irreversible
Money, records, and messages that go to your customers are drafted by the model and confirmed by a human before they happen. Models are sometimes wrong, often slow, and occasionally down; every path has a defined fallback, so the product degrades rather than breaks.
What this
includes.
- AI agent development
- Retrieval-augmented generation (RAG)
- AI assistants and copilots
- Document AI and extraction pipelines
- LLM evaluation suites
- AI feature integration into existing software
Straight
answers.
What is the difference between an AI agent and a chatbot?
A chatbot answers. An agent takes actions — it calls tools, queries systems, and completes multi-step tasks, then reports what it did. The engineering difference is that an agent can cause side effects, so it needs permissioning, an audit trail, and human approval on anything irreversible. We build agents with all three.
How do you stop an AI system from hallucinating?
You cannot stop a language model from generating text, so you constrain what it is allowed to generate from. Answers are grounded in retrieved passages and cite them; when retrieval returns nothing above threshold, the system refuses and offers a handoff instead of answering. A regression suite then measures refusal rate and citation coverage on every deploy, so the behaviour cannot quietly drift.
Can you add AI to software we already have?
Yes, and it is usually the better first project. Adding a grounded assistant or a document pipeline to a system already carrying your data is faster to ship and easier to evaluate than a greenfield build, because the test cases already exist in your production history.
Which models do you use?
Whichever meets the accuracy, cost, and latency budget for the task, and we benchmark rather than assume. Most production systems end up routing between a small fast model for the routine path and a larger one for the hard cases. We build so the model is a swappable dependency, not a load-bearing wall.
LET'S BUILD
Tell us what you're trying to build and we'll come back with a straight answer on scope, timeline, and whether we're the right people for it.
hello@algobit.dev