Custom AI Assistant Development (LLM-Based)
Custom AI Assistant Development is an engineering engagement that builds an LLM-based assistant grounded in your company knowledge and integrated with your systems. It is for teams whose people paste company questions into public chatbots and get generic answers, because no AI tool actually knows how your business works.
The problem this solves
Your team already uses AI, individually, inconsistently, and without context. Answers about your products, pricing rules, or processes come out generic or wrong because the model has never seen your documentation. Sensitive material gets pasted into consumer tools outside your control, and the knowledge that should make AI useful sits unindexed in drives, wikis, and inboxes.
How we work
We define the assistant around real recurring questions, not a demo script: which team asks what, from which sources the answers should come, and what a wrong answer costs. That scoping decides everything downstream, model choice, retrieval design, and where the assistant lives.
Then we build it: retrieval over your documents and data with vector search so answers cite your actual material, a commercial LLM such as Anthropic Claude or OpenAI models behind it, and integration into the surface your team already uses, Slack, a web app, or directly inside HubSpot. We build in Python and Node on serverless cloud infrastructure, the same stack we run our own AI tooling on, with logging, access control, and guardrails as first-class parts of the build.
Before handover we evaluate against a test set drawn from real questions and review the failures together. You get a working assistant, the pipeline that keeps its knowledge current, and documentation for operating it.
Deliverables
- Use case and source-of-truth specification
- Retrieval pipeline over your documents and data
- Working assistant integrated into Slack, web, or HubSpot
- Guardrails: access control, logging, refusal behavior
- Evaluation set from real questions with a reviewed test report
- Operations documentation and knowledge update workflow
What buyers ask before scoping.
What is the difference between an AI assistant and an AI agent?
An assistant answers and drafts; a human stays in the loop for every action. An agent executes multi-step work through APIs on its own, within guardrails. Assistants are the right first step for most teams, cheaper to build, easier to trust, and the retrieval layer they need is the same one agents use later.
Which model do you use, and where does our data live?
Model choice follows the use case, we work with commercial APIs such as Anthropic Claude and OpenAI models, and EU data residency options exist on the major platforms. Your documents stay in your retrieval store; commercial API terms exclude training on your data. We put the architecture and data flows in writing before building.
How do you stop the assistant from making things up?
Grounding plus honesty about limits: the assistant answers from retrieved company sources and is instructed and tested to say 'I do not know' when retrieval comes back empty. The evaluation phase measures exactly this behavior on real questions. No LLM system is hallucination-proof; a well-built one is caught-and-corrected by design.
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