Your product, now with a brain.
AI Integrations & Product Intelligence
We don't just add a chatbot. We deeply embed AI into your product workflows — automating decisions, surfacing insights, and creating experiences that feel like magic to your users.
Full-Spectrum Capabilities
Every layer of your product — owned, engineered, and optimised by our team.
Our Delivery Process
Data Audit
We assess your data quality, volume, and labelling — then map the highest-ROI AI use cases specific to your domain and user workflows.
3–5 daysModel Selection
Benchmark multiple models on your actual data — balancing accuracy, latency, cost, and privacy requirements before committing to a stack.
1 weekRAG / Fine-Tune
Build the knowledge base, embedding pipeline, retrieval layer, and (if needed) fine-tune a base model on your proprietary data.
2–4 weeksAPI Integration
Wrap the AI capability in a production API — streaming responses, error handling, rate limiting, caching, and usage metering.
1–2 weeksEvaluation & Red-team
Automated evaluation suites measuring accuracy, relevance, groundedness, and toxicity — plus adversarial red-teaming before launch.
1 weekProduction Monitoring
Continuous drift detection, output quality sampling, cost dashboards, and model upgrade pipelines to keep your AI sharp over time.
OngoingCase Studies
Real products, real metrics.
LegalTechContract Review — 90% Time Saving
Built a RAG-powered contract review tool that extracts clauses, flags risks, and compares against playbook standards — cutting review time from 4 hours to 20 minutes.
E-CommerceAI Recommendations — 35% Revenue Lift
Personalised product recommendation engine using collaborative filtering and real-time embeddings — driving a 35% uplift in revenue per session.
HealthTechClinical Notes AI — HIPAA Compliant
Ambient AI clinical documentation that transcribes doctor-patient conversations into structured SOAP notes, saving 2 hours per physician per day.
Technology Stack
LLM Providers
Frameworks
Vector DBs
ML / Training
Serving
Data / ETL
Monitoring
Safety
Common Questions
How do you choose between RAG and fine-tuning?+
RAG is the default for most use cases — it's cheaper, updatable, and citable. Fine-tuning is reserved for style/format adaptation, highly specialised domains, or when latency demands on-device inference. We benchmark both on your data before recommending.
How do you handle data privacy with LLMs?+
We default to private deployment (Azure OpenAI, self-hosted models) for sensitive data, implement PII redaction before any prompt is sent, and can deploy fully air-gapped models for regulated industries.
What accuracy can we expect from a RAG system?+
With proper chunking, embedding model selection, hybrid retrieval, and re-ranking, we consistently achieve 88–95% answer relevance on domain-specific Q&A. We measure this with automated eval suites before launch.
How do you prevent hallucinations in production?+
Multi-layer mitigation: grounding every response in retrieved context, using structured output with citations, post-processing consistency checks, and sampling-based output quality monitoring in production.
Can you integrate AI into our existing product without rewriting it?+
Yes. We expose AI capabilities as microservices with clean APIs that your existing product calls. We handle the full AI layer — data ingestion, model serving, and response formatting — independently of your core stack.
Let's make your product intelligent.
Book a free AI strategy call. We'll identify the highest-ROI AI use cases for your product, suggest the right models, and outline a 6-week integration roadmap.