Problem-First
We start with the problem, not with the AI feature. If a simpler solution exists, we use it. AI adds latency, cost, and complexity — it must justify all three.
Applied intelligence
AI & Automation Engineering
AI applied where it reduces real operational burden or unlocks measurable capability. Not added for marketing purposes. Not bolted on after the fact. Engineered into the system from the architecture stage.
Our position
How We Approach AI Integration
AI features must justify their complexity, latency cost, and infrastructure overhead. We evaluate fit before we write any code.
Production-Grade
Prompt engineering, fallback handling, rate limiting, error recovery, and cost monitoring are part of every AI integration. We do not demo-grade implementations.
Observable
Every AI pipeline includes logging, cost tracking, latency monitoring, and output validation. You can see what the system is doing and measure whether it is working.
Language models
LLM API Integration
We integrate large language model APIs — OpenAI, Anthropic, Google Gemini — into production web applications. The focus is on reliable, cost-controlled, and deterministic output for defined use cases.
Structured output, function calling, prompt versioning, and context window management are handled correctly. We do not pass raw user input directly to model APIs without appropriate validation and filtering.
- OpenAI GPT-4o / o1 integration
- Anthropic Claude API integration
- Google Gemini API integration
- Structured output and function calling
- Prompt template management
- Token cost monitoring and limits
- Streaming response handling
Providers
OpenAI / Anthropic / Gemini
Output format
Structured JSON / Streaming
Cost control
Token limits + monitoring
Fallback
Graceful degradation
Caching
Semantic + exact-match
Rate limiting
Per-user + global
Knowledge retrieval
Retrieval-Augmented Generation (RAG)
RAG systems allow language models to answer questions using your specific data — documentation, product catalogs, knowledge bases — rather than general training data alone.
Document Ingestion Pipeline
Automated ingestion and chunking of documents — PDFs, HTML, structured data — with embedding generation and storage in vector databases (Qdrant, pgvector, Pinecone).
Semantic Search
Vector similarity search combined with keyword search (hybrid retrieval) to surface the most contextually relevant documents before the LLM call is made.
Context Management
Intelligent context window packing, re-ranking of retrieved chunks, and prompt construction that maximises answer quality within token budget constraints.
Evaluation & Quality Control
Automated evaluation pipelines that measure retrieval precision and answer quality over time. RAG systems degrade without monitoring — we build the measurement in.
Trigger types
Event / Schedule / Webhook
Queue system
Redis / RabbitMQ / SQS
Workers
Python / Node.js / PHP
Observability
Structured logging + alerts
Retry logic
Exponential backoff
Dead letter queues
Yes — all critical queues
Idempotency
Enforced at job level
Background processing
Automation Pipelines
Event-driven workflows, scheduled batch jobs, and webhook-triggered processing that operate reliably without human intervention. Designed for failure: every pipeline includes retry logic, dead-letter handling, and alerting.
Common use cases: data synchronization, report generation, notification dispatch, third-party API polling, content transformation, and AI-enriched data processing at scale.
- Queue-based job processing
- Scheduled task management
- Webhook ingestion and routing
- Third-party API integration workflows
- Data transformation and enrichment
- Email and notification dispatch
Applications
Common Integration Patterns
These represent well-defined, production-tested use cases with measurable outcomes.
Content
AI-Assisted Content Operations
Automated summarization, translation, classification, and metadata extraction for content-heavy platforms. Reduces manual processing time while maintaining editorial control.
Search
Intelligent Search and Discovery
Semantic search over product catalogs, documentation, or knowledge bases. Users find what they're looking for using natural language rather than exact keyword matches.
Workflow
Automated Document Processing
Ingestion, extraction, classification, and routing of incoming documents. Structured data extracted from unstructured input and delivered to the right downstream system.
Support
Knowledge-Grounded Support Systems
Support interfaces backed by RAG over your documentation and historical support data. Reduces ticket volume for common queries while escalating accurately when human review is needed.
Have a Specific Automation Problem?
Describe the workflow or integration requirement. We'll tell you directly whether AI is the right tool, what the architecture looks like, and what it realistically involves.