AI development for real business workflows
Assistants that answer from your own documents, agents that complete tasks through your APIs, and LLM features inside the products you already run.
Overview
Why teams bring us in
Most AI projects stall between the demo and production. The model is rarely the hard part — retrieval quality, permissions, cost control, evaluation and fallbacks are.
We build those parts first, so the system behaves predictably when real users and real data reach it. We also run AI in our own product, an assistant platform with knowledge bases, messaging channels and voice agents, so our recommendations come from operating it, not from reading about it.
Deliverables
What we deliver
- 01
Knowledge assistants (RAG)
Chat and search over PDFs, websites, help-desk articles and internal documents, with source citations and access rules that follow your existing permissions.
- 02
AI agents and tool use
Agents that call your APIs to look up orders, book appointments, draft replies or update records — with a human approval step wherever an action affects money or customers.
- 03
Messaging and voice channels
Assistants on your website, WhatsApp, Instagram, Telegram, Slack or phone lines, with human handoff and a complete conversation history.
- 04
LLM features in existing products
Summarisation, classification, extraction and drafting added to your SaaS or internal tools through a provider-agnostic layer for OpenAI, Anthropic, Google Gemini or Groq models.
- 05
Evaluation and guardrails
Test sets built from your real queries, regression checks on answer quality, prompt-injection defences and output validation before a response reaches a user.
- 06
Cost and usage controls
Per-customer usage metering, model routing by task, response caching and rate limits so AI spend stays proportional to the value it creates.
How it fits together
The pipeline behind a dependable AI feature
Capabilities
- Your dataDocuments, websites, help desks, databases(used)
- IngestParse, clean, chunk and version content(used)
- IndexVector and keyword index with access metadata(used)
- RetrieveHybrid search filtered by user permissions(used)
- ReasonLLM answers or calls tools through your APIs(used)
- GuardrailsCitations, validation, PII masking, escalation(used)
- DeliverWeb, WhatsApp, Slack, voice or your app(used)
Typical projects
Where this work usually starts
- Customer support assistant with escalation to your team
- Lead qualification on WhatsApp and website chat
- Internal assistant for policies, SOPs and product knowledge
- Extracting structured data from invoices, forms and emails
- Voice agents for inbound call triage and appointment booking
- Reply drafting for support agents to review and send
Approach
How we work on it
- 1
Pick one measurable workflow
We start with a single workflow and agree how success is measured — resolution rate, handling time or hours saved — before choosing a model.
- 2
Prototype on your real data
A working prototype on a sample of your actual documents and questions shows early where retrieval or reasoning breaks.
- 3
Harden for production
Access control, evaluation suites, monitoring, fallbacks and cost limits are built in before launch, not added after the first incident.
- 4
Launch, review, improve
We review unanswered and low-confidence conversations on a regular cadence and improve content, retrieval and prompts from what we find.
Technologies we commonly use
- OpenAI
- Anthropic Claude
- Google Gemini
- Groq
- Vector search
- Laravel
- Node.js
- WhatsApp Business Platform
- Vapi
In our own products
Where we use these patterns ourselves
FAQ
AI Development: common questions
Which AI models do you work with?
We work with models from OpenAI, Anthropic, Google and Groq, and design integrations so the provider can be changed per task. The choice is driven by accuracy on your own test set, latency and cost — not vendor preference.
Can the assistant answer only from our content?
Yes. Retrieval-augmented generation limits answers to sources you approve, shows citations, and can be configured to say it does not know when your content has no answer.
How is our data kept private?
We use model provider API terms that exclude training on submitted data, keep your documents in storage you control, apply role-based access to retrieval and log access for audit. Sensitive fields can be masked before they reach a model.
What does a first AI project look like?
Usually a scoped pilot around one workflow: a prototype on your data, an evaluation against real questions, and a clear decision on whether and how to take it to production.
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Let’s talk about your AI Development project
Tell us about the problem, the users and any constraints. We will reply with questions and a suggested first step.