AI Integration for Startups

We build AI features that ship. Claude, OpenAI, and Azure integrated into your .NET/Azure stack with production-grade engineering.

Engineering-First AI Integration

We're .NET/Azure specialists who learned AI, not AI consultants learning .NET. That means production-grade error handling, cost management, and security—not just API wrapper code.

What We Build

We help startups add AI features that solve real problems, not features that just add buzz to your pitch deck.

🤖

Intelligent Automation

Agents that make decisions and take actions in your systems. Automated workflows that read, decide, and execute based on your business rules.

Examples: Support ticket triage and routing, invoice processing and validation, data enrichment pipelines, automated report generation
📄

Document Intelligence

Extract structured data from receipts, invoices, contracts, and forms. Validated outputs with schema enforcement—no hallucinations make it to your database.

Examples: Invoice data extraction, contract clause analysis, compliance document processing, form automation
💬

Conversational Interfaces

Production-ready chatbots and AI assistants. Context-aware conversations with proper state management and fallback handling.

Examples: Internal knowledge bots, customer onboarding wizards, guided troubleshooting, multi-turn data collection
🔍

Search & Discovery

Semantic search through unstructured data. Find relevant information even when exact keywords don't match. Knowledge bases with retrieval-augmented generation.

Examples: Documentation search, product discovery, research tools, internal wiki enhancement
✍️

Content Generation

Automated product descriptions, email drafts, report summaries. Template-driven generation with brand voice consistency and quality controls.

Examples: Product descriptions, email personalisation, report summarisation, content variation
👁️

Image Analysis

Analyse uploaded images for content moderation, accessibility descriptions, or product tagging. Extract text and structured data from visual content.

Examples: Receipt OCR, content moderation, accessibility descriptions, visual quality checks

Honest Assessment: If traditional code, rules engines, or SQL queries would work better, we'll tell you. AI isn't always the answer.

What Makes Our Approach Different

We treat AI APIs like any other external dependency: with the engineering rigour your production application deserves.

01

Validated Outputs

No tolerance for hallucinations. We use structured outputs with strict validation. If the AI response doesn't match your data model, it never reaches your database.

Type-safe validation with comprehensive schema checks
02

Proper Error Handling

AI APIs fail—rate limits, timeouts, invalid responses. We implement retry logic, circuit breakers, fallback strategies, and graceful degradation.

Exponential backoff, health checks, fallback workflows
03

Cost Monitoring

API costs add up fast. We implement token counting, caching strategies, and cost alerts. You'll know exactly what you're spending and why.

Usage tracking, prompt optimisation, response caching
04

Security-First Integration

Proper API key management, request/response logging for audit trails, PII handling, and Azure-native security patterns.

Azure Key Vault, encryption at rest, compliance logging
05

Observable & Debuggable

When AI features break, you need to know why. We implement comprehensive logging, prompt tracing, and performance monitoring.

Application Insights, structured logging, distributed tracing
06

Enterprise Integration

SSO authentication, role-based access to AI features, compliance requirements, and seamless integration with your existing Azure infrastructure.

Azure AD, managed identities, VNet integration

What We Do (And Don't Do)

✓ What We Do

  • Integrate OpenAI, Claude, and Azure AI APIs into production .NET applications
  • Build intelligent automation with tool-calling and decision logic
  • Implement document processing with LLM-based extraction and validation
  • Create conversational interfaces with proper fallback handling and context management
  • Design retrieval-augmented generation systems for knowledge bases
  • Optimise API costs through caching, prompt engineering, and smart routing
  • Monitor AI feature performance, costs, and quality in production
  • Implement security best practices for API key management and data handling
  • Provide honest assessments when traditional solutions would work better

✗ What We Don't Do

  • Custom model training or fine-tuning (we use pre-trained APIs)
  • Machine learning research or algorithm development
  • Data science, ML engineering, or deep learning infrastructure
  • "AI strategy consulting" without implementation
  • Multi-agent orchestration systems (not yet production-ready)
  • Recommend AI where traditional solutions work better

If you need ML engineering or data science, we'll tell you upfront and can recommend specialists. Our strength is making AI APIs work reliably in production .NET/Azure environments.

Learning in Public

We're building AI integration expertise through hands-on implementation. Here's what we've learned.

Building a Code Assistant Agent

Agentic AI Claude SDK .NET

Challenge

Understand modern agentic architecture patterns by reverse-engineering Claude Code's tool-calling system.

Implementation

Built .NET service orchestrating Claude API with tool-calling for file operations, bash execution, and git integration. Implemented reliable tool selection, error recovery, and context management.

Key Learnings

  • Error handling is 80% of the work: APIs fail, tools timeout, responses are malformed. Production readiness requires comprehensive error recovery.
  • Prompt engineering matters: Small wording changes in tool descriptions dramatically affect selection accuracy.
  • Context management is hard: Balancing conversation history with token limits requires careful truncation strategies.
  • Cost monitoring is essential: Without tracking, API costs can spiral on complex workflows.

Why This Matters for Clients

This proves we can build agents that interact with real systems reliably. Tool-calling patterns apply to any workflow automation: reading documents and writing to accounting systems, analysing support tickets and routing to teams, processing data and triggering business logic.

Full Transparency: This is a personal learning project, not client work. We're building AI integration expertise through hands-on implementation and sharing what we learn. When we take on your project, you benefit from these learnings without paying for our education.

Technologies We Work With

AI APIs & Services

  • OpenAI (ChatGPT, Vision, DALL-E)
  • Anthropic Claude (Sonnet, Haiku)
  • Azure OpenAI Service
  • Azure Document Intelligence
  • Azure AI Search
  • Azure Computer Vision

Search & Data

  • Azure AI Search (vector search)
  • PostgreSQL with pgvector
  • Redis vector similarity
  • Semantic search patterns
  • Retrieval-augmented generation (RAG)

Integration & Infrastructure

  • Modern .NET with C#
  • Azure Functions (serverless AI workflows)
  • Azure Service Bus (async processing)
  • Kubernetes (containerised deployments)
  • Application Insights (monitoring)

How We Work

Fixed-price projects with clear deliverables. No hourly billing surprises.

AI Feature Discovery

£5,000 - £7,000

Fixed-price technical assessment to validate your AI use case and design the implementation approach.

  • Use case evaluation and feasibility analysis
  • Model selection and cost analysis
  • Technical architecture design
  • Data pipeline and integration approach
  • Risk assessment and mitigation strategies
  • Fixed-price implementation quote
Deliverable: 2-week timeline, written technical report, recommended next steps

Ongoing AI Support

£3,000 - £6,000/mo

Post-launch optimisation and maintenance for production AI systems. Scope customised to your needs.

  • Prompt refinement and performance tuning
  • Cost optimisation and monitoring
  • Model updates and migration support
  • Feature expansion and iteration
  • Monthly performance and cost reports
Best for: Live AI features serving customers daily

Pricing Exclusions: All pricing excludes OpenAI/Claude/Azure API usage costs (you pay directly to providers with your own account). Discovery phase includes detailed monthly cost estimation with volume projections and optimisation recommendations.

Not sure which option fits? Book a free 30-minute consultation. We'll discuss your use case and recommend the right approach—or tell you honestly if AI isn't the best solution.

Implementation Pricing Tiers

Pricing varies based on complexity and integration requirements. All tiers include production-grade implementation.

Simple Integration

£8,000 - £12,000

Typical Timeline

2-3 weeks

Example Projects

  • Document classification system
  • Sentiment analysis API
  • Basic content moderation
  • Simple data extraction

Characteristics

  • Single AI model integration
  • Straightforward use case
  • Defined input/output schema
  • Standard error handling

Complex System

£18,000 - £25,000+

Typical Timeline

6-10 weeks

Example Projects

  • Multi-source RAG systems
  • Domain-specific assistants
  • Multi-step automation pipelines
  • Custom knowledge bases

Characteristics

  • Multiple AI models coordinated
  • Complex data pipelines
  • Advanced optimisation required
  • Enterprise integration patterns

All tiers include: Production-grade error handling, cost monitoring, security implementation, comprehensive logging, testing strategy, and full technical documentation. Discovery phase provides precise fixed quote for your specific requirements.

Let's Talk About Your AI Integration

Book a technical consultation. We'll discuss your use case, recommend the right approach (AI or not), and give you honest feasibility and cost estimates.

4-hour response · No obligation · Honest technical advice