AI built into production systems.
We build AI as one component of a working system, not a chatbot bolted on top. LLM integrations, retrieval pipelines, agents and computer vision, engineered to fail safely and stay inside a cost budget.
- Models
- OpenAI, Gemini, Anthropic, open-source vision
- Outputs
- Schema-validated before use
- Shipped in
- WhatsApp commerce, dating app trust scoring
Eight kinds of AI system we have designed and shipped.
Each one is a pattern we have built for a client, not a technology we have only read about.
Assistants and agents
Agents that take instructions, call APIs, query databases and run multi-step tasks. Built on structured outputs and tool use, not free-form chat.
Document question answering (RAG)
Search across your contracts, manuals or knowledge base, with answers that cite the passage they came from.
Conversational commerce
WhatsApp and chat flows for catalogue browsing, ordering and support, with a clean handoff to a person when needed.
Computer vision
Image classification, face liveness checks, content moderation and photo trust scoring for consumer and business apps.
Semantic search
Search over products, documents or records that understands what someone meant rather than matching keywords.
Operational summaries
Plain-language summaries of operational data, so owners and managers see what changed and what needs a decision.
Workflow automation
Trigger chains such as form submitted, AI classifies, record updated, WhatsApp alert sent, human review queued.
LLM integration
OpenAI, Gemini and Anthropic integrations with prompt versioning, rate limits, fallbacks and spend controls.
Demos work on good days. Production has to work on bad ones.
Production AI handles edge cases, fails gracefully, logs its decisions and stays inside latency and cost budgets. That takes engineering, not just an API key.
- Prompts are versioned and reviewed like code.
- Fallback chains when a model call fails or times out.
- Structured outputs validated with Zod or Pydantic before anything downstream sees them.
- Rate limits and cost monitoring so API spend can't run away.
- Human review built in for decisions that need it.
- Evaluation sets that measure answer quality over time.
- 1
User input
WhatsApp message, form or API call
- 2
Validate and sanitise
Zod schema, length limits, injection checks
- 3
Classify intent
Model call with structured output
- 4
Retrieve context
Vector search over products or documents
- 5
Generate
Response grounded in the retrieved context
- 6
Validate output
Schema check before it is sent
- 7
Act
Database write, API call or WhatsApp reply
- 8
Log
Tokens, latency and decision audit trail
AI is only useful when it touches real systems.
Your database, your WhatsApp account, your customer records. These are the places we wire it in.
- CRM & ERP
- Update recordsSummarise activityFlag anomalies
- Take ordersAnswer FAQsEscalate to staff
- Documents
- ContractsReportsKnowledge basesCited answers
- Websites & apps
- SearchRecommendationsEmbedded assistants
- Internal dashboards
- Metric summariesAnomaly flagsWeekly reports
- Customer support
- Tier 1 resolutionRouting to the right person
Describe the AI problem you want solved.
We'll design the architecture, say plainly whether AI is the right tool, and give you a fixed-price scope within 24 hours.
- Written scope
- Within 24 hours
- Pricing
- Fixed per milestone
- Ownership
- 100% of code and IP