Part of Generative AI & LLM

AI copilots

In-product copilots that draft, explain, analyse and act inside your SaaS or internal tools, with context from the user’s data.

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In one paragraph

AI copilots: In-product copilots that draft, explain, analyse and act inside your SaaS or internal tools, with context from the user’s data. 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.

How ai copilots fits into generative ai & llm

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.

How we deliver

1
Use-case and model selection
Define the job, quality bar and constraints; compare candidate models on your data, not benchmarks.
2
Prompt and retrieval design
Design prompts, context, tools and grounding, with an evaluation set built from real examples.
3
Build the API layer
A robust service with streaming, caching, rate limits, observability and cost tracking.
4
Evaluate and harden
Automated and human evaluation, red-teaming for prompt injection and unsafe output, and fallbacks.
5
Ship and iterate
Release behind flags, watch usage and quality dashboards, and upgrade models when the evals say so.

Frequently asked

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.