AI, Data & Intelligence
AI Copilots
AI copilots embedded inside your own software product or internal platform, helping your users complete tasks in context, with features designed, priced and governed as part of the product.
Capability overview
What ai copilots involves
A copilot lives inside a product and understands the screen the user is on: it can explain a dashboard, draft a configuration, write a query against the user's data or walk them through a complex form. For software companies it is a product feature with its own roadmap, pricing implications and support model.
Building an in-product copilot involves product decisions as much as AI engineering: which tasks it should own, how users see and undo its actions, how usage is metered per plan, and how tenant data stays isolated. We work with product managers and designers on those choices and with engineers on the implementation.
What is included
Copilot design areas
Contextual awareness
The copilot receives the current page, selected records and user role, so suggestions are specific to what the user is doing.
Action design
Actions such as creating a report or changing settings shown as previews the user confirms, with an undo route.
Tenant isolation
Retrieval, caching and logs partitioned per customer so one tenant's data can never inform another tenant's answers.
Plans and metering
Usage limits and credits aligned to your subscription plans, with telemetry to understand the cost of each customer's usage.
Product analytics
Acceptance rate of suggestions, task completion and retention impact measured to guide the copilot roadmap.
How we work
How we deliver ai copilots
Opportunity mapping
Product usage data and support tickets analysed to find tasks where users struggle or spend the most time.
Interaction prototyping
Designers prototype copilot interactions and test them with customers before engineering starts.
Feature build
Copilot services, context assembly and UI components built into your codebase and release process.
Private beta
Selected customers use the copilot while acceptance rates and failure cases are tracked.
Launch and packaging
General release with plan limits, documentation, support playbooks and a quarterly improvement cycle.
Related capabilities
Related capabilities in Generative AI & LLM Engineering
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LLM-Powered Agents within our generative ai & llm engineering services, scoped after a short discovery conversation.
Prompt Engineering
Systematic design, testing and versioning of the instructions that drive language model behaviour, so outputs are accurate, consistently formatted and stable when models or inputs change.
LLM Integration
Adding large language model capabilities to existing applications through provider APIs, handling streaming, structured output, rate limits, retries, data redaction and switching between providers.
Model Fine-Tuning
Adapting language models to your tasks, terminology and output formats with supervised fine-tuning and parameter-efficient methods such as LoRA, when prompting and retrieval are not enough.
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Where this applies
Healthcare & Life Sciences
Technology systems for regulated environments where privacy, auditability and continuity…
Manufacturing & Industrial
Connected operations, asset, field, supply chain and industrial platforms for complex operating…
Banking, Financial Services & Insurance
Technology systems for regulated environments where privacy, auditability and continuity…
E-Commerce
Digital platforms for customer experience, operations, commerce, content, marketing and service…
Questions & answers
Questions about AI Copilots
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionNot necessarily. Inline suggestions, one-click actions and smart defaults are often more useful than an open chat box. Chat works best as an addition for open-ended questions.
Common approaches include bundling it in higher plans, offering usage credits or charging an add-on fee. Telemetry on cost per customer during the beta helps choose a model that protects margins.
Yes. We build within your repositories, frameworks and deployment pipelines, following your code review and security practices, so your team can maintain it afterwards.
A focused first version covering two or three high-value tasks usually reaches private beta in ten to fourteen weeks.
Next step
Discuss ai copilots with Acmez
Share what you need to change, build, integrate or support. We will map the practical next step.