Generative AI & LLM
Generative AI & LLM development services
Put GPT-class models to work inside your product and operations.
In one paragraph
Generative AI and LLM services put models such as GPT, Claude, Gemini and open-source LLMs to work inside your software: custom assistants and copilots, AI search, writing and summarisation tools, recommendation and personalisation engines. We handle prompt engineering, multi-model orchestration, evaluation and cost control so features ship reliably and stay accurate as models change.
Why it pays off
- New product capabilities shipped in weeks
- Answers with sources instead of searching
- Predictable token spend and latency
- Quality measured with evaluation sets, release over release
- Data stays inside your boundary
What's included
Generative AI & LLM: everything in this service
Custom GPT-powered applications
Purpose-built applications on GPT, Claude or open models with your data, workflows and interface, not a generic chat window.
AI assistants
Task-focused assistants for teams (sales, legal, finance, engineering) grounded in your documents and systems.
AI search engines
Natural-language search over products, content or knowledge with synthesised answers and citations.
AI writing systems
Brand-consistent drafting for proposals, reports, product descriptions and support replies with review workflows.
Enterprise GenAI tools
Secure internal GenAI platforms with access control, audit logs, model routing and usage governance.
Multi-model orchestration
Route each request to the best model by quality, cost and latency, with fallbacks and evaluation.
Prompt engineering
Systematic prompt design, versioning and evaluation harnesses so quality is measured, not guessed.
AI personalisation engines
Content, offers and experiences tailored per user with LLMs and behavioural data.
AI summarisation
Summaries of calls, documents, threads and reports at the length and format each audience needs.
AI recommendation systems
Hybrid recommenders combining embeddings, behavioural signals and LLM explanations.
AI content generation
Scaled content pipelines with brand guardrails, fact checks and human approval.
AI translation
Domain-tuned translation and localisation with glossary control and quality review.
AI documentation tools
Generate and maintain technical and process documentation from code, tickets and conversations.
Use cases
Where it creates value
In-product copilot
Ship an assistant inside your app that answers, drafts and acts on the user’s data: a feature customers pay for.
Search that answers
Replace keyword search with answers and citations across help centres, catalogues and knowledge bases.
Drafting at scale
Proposals, reports, listings and replies drafted from your data in your voice, reviewed by people.
Summaries everywhere
Meeting, ticket and document summaries pushed to the right place automatically.
Personalised experiences
Recommendations and content adapted to each user with explanations they trust.
How we deliver
How we deliver generative ai & llm
- 01
Use-case and model selection
Define the job, quality bar and constraints; compare candidate models on your data, not benchmarks.
- 02
Prompt and retrieval design
Design prompts, context, tools and grounding, with an evaluation set built from real examples.
- 03
Build the API layer
A robust service with streaming, caching, rate limits, observability and cost tracking.
- 04
Evaluate and harden
Automated and human evaluation, red-teaming for prompt injection and unsafe output, and fallbacks.
- 05
Ship and iterate
Release behind flags, watch usage and quality dashboards, and upgrade models when the evals say so.
Related work
Outcomes, not demos.
Frequently asked
Generative AI & LLM: questions we get
We choose per use case: hosted APIs for speed and quality, open models such as Llama, Mistral and Qwen when data residency, cost at scale or offline use matters. Many systems mix both, and we build so that switching models later is a configuration change, not a rewrite.
Ground answers in retrieved, approved content; constrain scope and tone; require citations where facts matter; and measure hallucination rate on an evaluation set. For high-stakes outputs we add a human review step. No system is perfect, so we design the failure mode to be visible.
Private deployments or no-training API tiers, encryption in transit and at rest, per-user access filtering, secret management and audit logging. We document the data flow before writing code and can keep everything inside your cloud account or on-prem when required.
Token spend depends on volume, model and how much context each request needs. We estimate from your expected usage during design, then reduce cost with caching, smaller models for simple steps and prompt optimisation. Cost dashboards are part of every delivery.
A well-scoped feature such as summarisation or an in-app assistant can reach a production pilot in four to eight weeks. The evaluation set and API layer built for the first feature make the next ones faster. We recommend a small pilot group and clear success metrics from the start, so the decision to roll out is based on evidence rather than a demo.
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