Service
AI and Machine Learning Integration
We build AI solutions that move beyond demos into production. From RAG-powered knowledge bases to autonomous agents, we integrate intelligence where it creates real business value.
We integrate artificial intelligence into products that ship to production — not demos that live in notebooks. From RAG pipelines to autonomous agents, we build AI features that solve real problems and generate measurable ROI.
What's included
- Custom AI chatbots and virtual assistants
- RAG systems for knowledge base search
- Document processing and data extraction
- Predictive analytics and recommendation engines
- LLM fine-tuning and prompt engineering
Key metrics
48h
Feasibility assessment
> 90%
Accuracy baseline
100%
Evals before deploy
Multi
Model providers
How We Build It
A layered production architecture designed for reliability, performance, and maintainability.
User Interface
AI SDK Streaming
Edge runtime
LLM Router
OpenAI / Anthropic
Vector DB
Pinecone / pgvector
Document Store
Embeddings + metadata
Monitoring
Langfuse / Sentry
Architecture varies per project — this represents our standard production pattern for this service.
Our Process
Use-Case Scoping
Define the problem, success metrics, and the data available before selecting a model or architecture.
Evaluation Harness
Build ground-truth datasets and automated evaluation pipelines before any model is deployed.
Prototype and Test
Rapid iteration on prompts, retrieval strategies, and model selection with measurable benchmarks.
Production Hardening
Observability, cost controls, prompt injection defences, and staged rollout to real users.
Technology Stack
Why clients choose us
- End-to-end ownership — no agency hand-offs
- Architecture documented before any code is written
- Tests written alongside features, not as an afterthought
- Fixed-price phases with clear deliverables
What We Build
AI Chatbots and Assistants
Customer support bots, internal knowledge assistants, and domain-specific copilots. Grounded in your data using RAG, not generic LLM responses.
RAG and Knowledge Systems
Retrieval-augmented generation systems that let your users query internal documents, product catalogues, and knowledge bases using natural language.
Document Processing and Extraction
Automated pipelines that read, classify, and extract structured data from PDFs, contracts, invoices, and unstructured text at scale.
Recommendation and Personalisation Engines
Collaborative filtering, content-based recommendation, and hybrid models that surface the right product, content, or action for each user.
AI-Assisted Workflows and Copilots
Productivity features embedded directly into your existing product — smart autocomplete, draft generation, data summarisation, and intelligent form assistance.
Our Approach to AI in Production
Model Selection Without Vendor Lock-In
We evaluate OpenAI, Anthropic, Google, and open-source models for each use case. We pick the right tool, not the most fashionable one.
Evaluation Before Deployment
We build evaluation harnesses before deploying AI features. Every model output is tested against ground truth before it reaches your users.
Observability and Cost Control
AI features are expensive to run carelessly. We instrument every LLM call with logging, latency tracking, and cost monitoring from day one.
Keeping Up With Model Releases
The AI landscape changes monthly. We maintain a structured process for evaluating new models, running shadow tests, and upgrading safely without breaking production.
Security and Compliance
We implement prompt injection defences, output sanitisation, and data isolation to ensure AI features cannot be used to expose sensitive information or bypass business logic.
Technologies
OpenAI, Anthropic Claude, LangChain, Vercel AI SDK, Pinecone, pgvector, Python, Node.js.
Start Your AI Project
Tell us the problem you want AI to solve. We will assess feasibility, propose an architecture, and identify the right model and approach within 48 hours.
From the blog
Insights on AI and Machine Learning Integration
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Building SaaS Products With AI Features: Architecture Guide
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Security Vulnerabilities Introduced by AI Models: What Developers Must Know
Prompt injection, data leakage, insecure tool use, and supply chain risks. The security threats specific to AI-integrated applications and how to defend against them.
Ready to get started?
Let's build your intelligent systems that deliver measurable roi.
Tell us what you need. We respond within one business day with a technical plan and realistic timeline — no sales pitch.
Start Your Project