Custom GenAI products, built to ship — not just demo.
A GenAI demo is easy; a GenAI product is hard. The companies winning in 2026 are the ones who got GenAI into production with governance, accuracy and adoption. We build LLM applications, copilots and content systems — integrated into your stack, hardened with guardrails, and adopted on-site.
Generative AI development is the practice of building applications on large language and multimodal models that create text, code, images or structured output — then integrating, governing and operating them in production.
It goes far beyond calling an API: it means choosing the right model, grounding it in your data, adding guardrails and evaluation, and wiring it into your systems so it's reliable and cost-controlled.
Key areas
Prompt engineering & orchestration
RAG grounding
Fine-tuning / adapters
Multi-model routing
A GenAI demo is easy; a GenAI product is hard. The companies winning in 2026 aren't the ones with the flashiest prototype — they're the ones who got GenAI into production with governance, accuracy and adoption. That gap between demo and durable product is exactly where most projects stall, and where experienced delivery matters.
Six GenAI capabilities — from LLM apps and copilots to multimodal systems and fine-tuning
AI copilots and assistants embedded in your product or operations — helping your users write, decide and act faster without leaving your existing system.
Drafting, summarisation, classification and extraction at scale — automated content pipelines that use your data and match your brand voice.
Applications that work across text, image and document understanding — from visual content generation to image-to-text extraction and multimodal search.
We select the right model for your accuracy, privacy and cost profile — and fine-tune or adapt when domain tone or precision demands it.
Safety testing, output filtering, measurable quality evaluation and monitoring so your GenAI feature behaves reliably and within policy — not just in demos.
GenAI capabilities wired into your existing data and systems via APIs and MCP — so users get the benefit without changing workflows.
For fast, controllable results without model changes.
Keep output accurate and citable from your own documents.
When domain tone or precision demands it.
Balance quality and cost across model tiers.
Measurable quality before launch, not after.
Models: GPT, Gemini, Claude, Llama, Mistral · Frameworks: LangChain, LlamaIndex, LangGraph · Retrieval: Pinecone, Milvus, FAISS · Evaluation-driven development
From feasibility through on-site adoption — six stages from prototype to production
Define the use case, data sources and success metric — what the GenAI feature must do, what inputs it takes, what outputs it produces and how we measure success.
Build vs API, fine-tune vs RAG, open-source vs frontier model — we balance speed, control, privacy and cost and recommend honestly.
Build a working prototype with measurable quality on real use cases. Evaluate accuracy, safety and edge-case handling before investing in production build.
Add guardrails, security controls, cost controls (model routing, caching) and output evaluation to make the system reliable and cost-predictable in production.
Production deployment integrated into your existing stack — web, mobile or backend — with monitoring, usage tracking and feedback loops.
We stay until your team is actually using the GenAI feature daily — tracking utilisation and iterating until adoption targets are met.
Production focus, model-agnostic, privacy-first — shipped not slideware
We get GenAI past the demo into reliable, governed use. Our GenAI voice-and-chat PMS built on Gemini is in production — not a prototype sitting on a shelf.
The right model for your use case — GPT, Gemini, Claude or open-source — with spend kept predictable through routing, caching and evaluation.
Your data stays yours — least-privilege access, no unintended training on your data, content guardrails, and on-prem/private-cloud options for sensitive workloads.
We stay on-site after go-live and measure feature utilisation. A GenAI feature your team isn't using isn't done — we iterate until adoption targets are met.
We design and build the feature, integrate it into your product and embed with your team — no handoff gap between strategy, build and adoption.
A focused GenAI prototype ships in 3–6 weeks — fast enough to validate with real users before committing to a full-scale production build.
Natural-language project management in production — run your day by talking or typing.
A scoped GenAI prototype delivered at a fixed price.
Flexible build-and-iterate for evolving GenAI requirements.
An embedded team for production GenAI app development.
Prototype in 3–6 weeks · Production GenAI app in 2–4 months
Everything you need to know about generative AI development
Model-agnostic — GPT, Gemini, Claude or open models, selected per use case.
Whichever fits — we balance speed, control, privacy and cost and recommend honestly.
RAG grounds answers in your live documents; fine-tuning adapts the model's behaviour. Many products use both.
Your data stays in your environment where required, with least-privilege access and no unintended training on it.
Model routing, caching and evaluation keep quality high and token spend predictable.
Guardrails, RAG grounding, evaluation sets and human review for sensitive flows.
A prototype in 3–6 weeks; a production app in 2–4 months.
Production LLM apps, copilots and content systems shipped for real businesses
Book a discovery call. We'll tell you what's buildable, how long it takes and what it would cost — no commitment required.
