Generative AI Integration
Put production-grade GenAI inside your product and operations
From AI search over your documents to copilots inside your SaaS, we take generative AI from demo to dependable: model selection, RAG pipelines, evaluation, cost control and security handled by engineers who ship.
What is generative AI integration?
Generative AI integration is the engineering work of embedding large language models into a company's products and internal operations — safely and at production quality. It covers selecting the right models (OpenAI GPT, Anthropic Claude, Google Gemini or open-weight alternatives), building retrieval-augmented generation so outputs are grounded in company data, designing prompts and evaluation suites, controlling latency and cost, and wrapping everything in security and access controls. The outcome is AI features users can rely on, not just impressive demos.
What this gives your business
- Ship AI features your users actually trust and use
- Search and summarise your documents with grounded answers
- Generate on-brand content, descriptions and reports at scale
- Choose models on evidence — quality, latency and cost benchmarks
- Stay secure: access control, PII handling and audit baked in
What's included
Everything a production-grade build needs
RAG & knowledge pipelines
Chunking, embeddings, vector search and reranking tuned to your corpus so answers cite the right source, every time.
In-product copilots
Assistants embedded in your SaaS or internal tools that understand context, take actions and respect user permissions.
Content generation systems
Product descriptions, reports, emails and creative drafts with brand-voice control, factual checks and human review flows.
Model strategy & evaluation
Side-by-side benchmarks on your tasks, automatic eval suites and regression tests so quality is measured, not assumed.
Cost & latency engineering
Caching, routing between models, prompt compression and batching to hit your budget and speed targets.
Security & compliance
PII redaction, prompt-injection defences, tenant isolation and audit logs designed in from the start.
How it happens
Our delivery process, step by step
- 1
Use-case sprint
Identify and rank GenAI opportunities by feasibility and business value; pick the first winner.
- 2
Prototype & evaluate
A working prototype on your data with quality benchmarks, not vibes.
- 3
Production hardening
Guardrails, evals, monitoring, fallbacks and cost controls to production standard.
- 4
Integration & rollout
Embedded into your product or workflow with phased exposure and feedback loops.
- 5
Operate & improve
Usage analytics, model updates and continuous evaluation keep quality rising.
Use cases
Where it earns its keep fastest
AI answers over company knowledge
Staff and customers ask questions in plain language; answers come grounded with citations.
SaaS copilot features
Differentiate your product with assistants that draft, configure and explain inside the UI.
Catalogue & content operations
Thousands of product descriptions, translations and variants generated under editorial control.
Meeting & document summarisation
Calls, contracts and reports condensed into decisions, risks and action items automatically.
FAQ
Generative AI: your questions, answered
Which is better for us — OpenAI, Claude or Gemini?
It depends on the task, and the honest answer comes from benchmarks on your data. In our evaluations, different models lead for different jobs — long-document reasoning, structured extraction, multilingual output, cost at scale. We test the candidates on your real tasks and design so you can switch later without a rebuild.
How do you prevent hallucinations in production?
Grounding through retrieval (the model answers from your documents, with citations), constrained output formats, automatic fact-checking against sources, confidence thresholds with graceful fallbacks, and evaluation suites that catch regressions before users do. For high-stakes outputs we add human review steps.
Will our data be used to train public models?
No. We use enterprise API tiers where providers contractually do not train on your data, configure zero-retention options where available, and can deploy open-weight models in your own cloud for the most sensitive workloads.
Can you work with our in-house development team?
Gladly. We often deliver the AI core — pipelines, prompts, evals, guardrails — while your team owns the surrounding product, with pairing and documentation so ownership transfers cleanly.
How do you keep API costs under control?
Cost is an engineering target from day one: right-sizing models per request, caching frequent answers, trimming context, batching background jobs and routing simple queries to cheaper models. You get a cost dashboard, alerts and a monthly efficiency review.
Pairs well with
Services that compound this one
Ready to explore generative ai for your business?
Book a free consultation — we'll demo something close to your use case live and give you a clear scope, timeline and price.