BLUEBERRY Intelligence & Automation , home

BUILD

AI & intelligent systems

Turn the opportunity into a working system.

We design and build AI applications, knowledge assistants, automations, integrations and the supporting software around them.

Discuss a Pilot Build Email us

When it fits

Is this the right starting point?

You know what should improve and need a dependable system, not another demo.

Scope

What we do

  • AI applications & internal tools

    Focused software that removes a specific operational bottleneck.

  • Knowledge assistants (RAG)

    Assistants that answer from your own documents and show their sources.

  • Workflow automation

    Automations with clear checkpoints where a person reviews or approves.

  • Integrations

    Connecting AI models to the systems and data you already use.

  • Backend & APIs

    The services, data handling and access control a real system needs.

  • Deployment

    Containerized deployment, basic monitoring and a documented release process.

What you receive

  • Working system: Deployed, tested and documented code that your organization owns.
  • Evaluation checks: A repeatable set of test questions or cases to catch regressions.
  • Runbook & handover: How to operate, update and troubleshoot it, walked through with your team.

How it runs

A scoped delivery sprint, with milestones agreed after discovery and technical scoping.

It starts with a short call to confirm scope. You then receive a written proposal tailored to the work.

Build · ready-to-deploy

Website Support Assistant

Visitors get quick answers about your services, hours and policies, drawn only from documents you approve. When it does not know, it says so and offers one-tap WhatsApp, email or call buttons to reach your team.

You can try it right here. The assistant on this website is the same product we install for clients, set up with Blueberry's own documents.

Designed around your content

Clear answers from approved sources.

Built for your workflow

A tailored handoff when the answer needs a person.

  • Ownership: You own it. Hosting (a small cloud server) and AI usage are billed to you directly by the providers, with no per-message platform fees and no lock-in.
  • Support: Optional updates, prompt adjustments, and uptime monitoring are available when needed.
Discuss your needs

What it does

  • Answers only from the documents you provide (PDF, Word, Excel/CSV, PowerPoint, HTML, Markdown or text) and shows the sources behind each answer
  • Finds exact details reliably, such as product codes and phone numbers, using keyword plus semantic search
  • Hands over to your team with WhatsApp, email and call buttons when the answer is not in your documents
  • Added to your website with a single script tag, styled with your colors, logo, greeting and starter questions
  • A password-protected admin page to upload or remove documents and test answers
  • One private instance per client: your documents and chat history stay on your own server

It answers questions and hands over to people. It does not take bookings, and it is not a WhatsApp bot.

Related work

Systems built with this approach

Founder-built systems, described without client names or unpublished results.

Customer support Hybrid search Built

Multi-tenant customer support platform

Problem
General-purpose chatbots gave confident but wrong answers and could not keep each organization’s rules and documents separate.
Approach
Strict tenant isolation, hybrid retrieval (keyword + dense vector search) and local re-ranking, so answers come only from the right organization’s documentation.
System
A support platform that turns each organization’s documentation into grounded answers and hands complex requests to people.
  • Python
  • FastAPI
  • Hybrid search
  • Re-ranking
  • Next.js
  • TypeScript
Governance & operations Local transcription Built

Privacy-first meeting minutes

Problem
Organizations wanted structured minutes from confidential meetings without sending raw recordings to a cloud service.
Approach
Transcribe audio locally, remove personal information on the device, and only then use a language model to structure the redacted text.
System
A meeting tool that produces minutes, decisions and action owners while keeping raw audio and unredacted transcripts on the machine.
  • Python
  • Whisper (local)
  • spaCy
  • Gemini API
Education LLM-as-judge checks Built

Curriculum-aligned lesson planning assistant

Problem
Teachers spend a lot of time writing lesson plans and assessment material that match the curriculum.
Approach
Generate lesson plans and assessments from curriculum data, then run an automated review step (LLM-as-judge) that checks each output against the syllabus before it is shown.
System
A lesson-planning platform for secondary teachers that drafts curriculum-aligned plans and assessments for teacher review.
  • Python
  • FastAPI
  • Supabase
  • LLM-as-judge evaluation

Ready to talk about build?

Tell us what you are trying to improve. We reply within two business days.

Discuss a Pilot Build ada@blueberryia.com