AI in your product

    AI content & search setup

    We help your product answer from your own documents and keep answers consistent, not just in the sales demo.

    Prompts need the same discipline as production code

    A prompt that works in a sandbox often breaks when real users phrase questions differently. Teams end up rewriting the same instructions in Slack because nobody tracked what changed or why quality dropped.

    We version prompts, test them against real questions, and review changes before they ship. Your team gets templates and a process, not a black box only one developer understands.

    Search your documents, not the whole internet

    When AI should answer from your manuals, policies, or support history, how documents are split and indexed matters more than which model you pick. We structure content so production questions do not pull finance paragraphs into a support answer, or vice versa.

    We have done this for manufacturers, marketplaces, and B2B tools. The pattern is the same: organise your source material first, then connect the model to the right slice of it.

    Test before and after launch

    We build a set of 50 to 200 test questions with expected answers or grading rules. Every prompt change runs against that set. After launch, wrong answers from real usage get added so quality improves instead of drifting.

    Tools we typically use

    We adapt to your stack when needed. These are common on projects like this.

    OpenAI / Claude / Gemini
    Document search (Pinecone, pgvector, or similar)
    Python / Node.js

    Common questions

    Do we need RAG or can we fine-tune instead?
    For most products RAG is faster to ship and easier to update when documents change. Fine-tuning makes sense when you need a specific tone or format and your training data is stable. We help you pick based on your data and update frequency.
    Can our team maintain prompts after handover?
    Yes. We document every template, variable, and eval criterion. Most clients run eval scripts themselves within a week of handover.
    What if our documents are messy or outdated?
    We flag that upfront. Garbage in still means garbage out. Part of the engagement is often a cleanup pass: removing duplicates, fixing headings, splitting merged PDFs, before indexing.

    Related services

    More in AI in your product

    AI integrations

    We add OpenAI, Claude, Gemini, or similar AI to your product with spending controls, backups when a provider fails, and a setup that stays secure after launch.

    Custom chatbots

    Support bots and internal assistants trained on your data, linked to your CRM, and deployable on cloud or your own servers when required.

    Start a project

    Tell us what you need built

    One call is enough to see if we are the right team. If we are, you get a written scope before any contract, so you know cost and timeline upfront.

    NDA availableNo hard sellReply within 2 business hours

    What happens next

    3 steps
    01

    15-minute call

    You explain the problem. We ask questions. No slide deck from us.

    02

    Written scope in ~48 hours

    Timeline, team shape, milestones, and price range for you to review.

    03

    Kickoff with your squad

    PM, tech lead, shared Slack or Teams, and repo access from day one.