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AI, Data & Emerging Technology · AI copilots

Copilots built into the tools your team uses

An AI copilot sits inside your product or internal app and helps people work faster: drafting, summarizing, searching your knowledge and suggesting next steps, while the person keeps the final say on every decision.

  • Grounded in your data
  • Context-aware suggestions
  • Permission-respecting search
Start your projectHow we work

Overview

Designing an assistant people actually keep using

A copilot is an assistant that sits beside a person while they work, offering drafts, answers and next steps without taking control. The person stays responsible for the result. That framing shapes everything: the copilot has to be fast, show where its suggestions came from, and be easy to ignore when it is wrong. Trust grows when its sources are always one click away.

The hardest decisions are about context. What should the copilot see at any moment: the open record, the user’s recent activity, the whole knowledge base? More context can improve answers but adds cost, latency and permission risk. We define a context budget per screen and test which sources measurably improve the output before adding them. Sources that do not help are left out.

Copilots succeed when they save a few minutes many times a day. Adoption, edit rates and dismissals tell you far more than a launch demo does. We track those signals from the first week, and we treat a suggestion people routinely delete as a design problem to fix, not a user problem to train away.

Who it’s for

Built for teams like yours

  • 01

    Teams buried in documentation

    Support, compliance, HR and operations staff who search across manuals, policies and past cases dozens of times a day and need answers with the source attached.

  • 02

    SaaS product owners

    Software companies that want to add an in-app assistant for their own customers, helping them configure features, write content or understand reports without leaving the product.

  • 03

    Sales and account managers

    Customer-facing teams who need quick account summaries, call prep notes and follow-up drafts pulled from CRM history, email threads and meeting notes they already have.

Why it matters

Help where the work happens

Generic chat tools sit in another tab and know nothing about your customers, policies or records. A copilot built into your own software sees the screen the user is on, respects their permissions and answers from your documents, so suggestions are relevant and staff stop copying data between windows.

Because the person approves every output, copilots are often the lowest-risk way to put AI to work, and a sensible first step before giving any system more autonomy over your data or customers.

Every engagement includes

  • Use-case workshopwe find the tasks where assistance saves real time and rank them by value.
  • Knowledge pipelineingestion, chunking and indexing of the sources the copilot should draw on.
  • Interface designa side panel, command bar or inline UI that fits your existing product.
  • Access controlssingle sign-on and document-level permissions carried through to every answer.
  • Quality testingquestion sets with expected answers, reviewed before launch and after changes.
  • Handover & trainingdocumentation for admins and short guides that help staff use it well.

Features

What makes a copilot useful

  1. 01

    Grounded in your data

    Retrieval over your docs, tickets and records so answers cite real sources instead of guessing.

  2. 02

    Context-aware suggestions

    The copilot knows which customer, case or document is open and tailors its help accordingly.

  3. 03

    Permission-respecting search

    Users only get answers from content they are already allowed to see in your systems.

  4. 04

    Inline drafting

    Replies, notes, summaries and reports drafted in place, ready for a person to edit and approve.

  5. 05

    Feedback capture

    Thumbs up, edits and rejections are recorded so prompts and retrieval improve over time.

  6. 06

    Cost and latency control

    Caching, model routing and streaming responses keep the experience quick and spending predictable.

In practice

Where a copilot fits in the day

  • Support reply assistant

    While an agent reads a ticket, the copilot surfaces similar resolved cases and relevant help articles, then drafts a reply in your tone that the agent can edit and send.

  • Policy and procedure lookup

    Staff ask how to handle a specific situation and get a short answer quoting the exact section of the handbook or SOP, with a link to open the full document.

  • In-product setup helper

    Your customers describe what they want to achieve and the copilot walks them through the right settings, filling in fields where permitted and explaining what each option does. Changes are always shown before they are saved.

  • Meeting and account recap

    Before a call, the copilot compiles recent emails, open tickets and notes on the account into a one-page summary with suggested talking points and open questions. It draws only on records the user can already open.

Process

How we work

  1. 1

    Shadowing sessions

    We watch people work, note every time they search, copy or switch tools, and pick the two or three moments where an assistant would save the most effort. Those moments become the first release scope.

  2. 2

    Content ingestion

    Documents, tickets and records are connected through a pipeline that cleans, chunks and indexes them, keeping each item’s access permissions attached for filtering at query time. Sources can be added one at a time, so each is tested before the next arrives.

  3. 3

    Interface placement

    We prototype the assistant as a side panel, inline suggestion or command palette inside your tool and test which placement people reach for without being reminded. The right placement usually matters more than any extra feature.

  4. 4

    Answer quality testing

    Real questions from your team are paired with approved answers, and every retrieval or prompt change is scored against that set before it reaches users. Scores and failed questions are reviewed with your subject experts.

  5. 5

    Adoption tracking

    After launch we monitor usage, accepted versus edited suggestions and thumbs-down feedback, then tune sources and prompts where answers fall short of what people need. Findings are shared with you in a short monthly review.

Deliverables

What you receive

  • Prioritized list of assist moments
  • Permission-aware search index
  • Copilot interface built into your tool
  • Golden question set with approved answers
  • Feedback capture and review queue
  • Usage, latency and cost reporting
  • Admin guide for adding new sources

Tools & methods

Retrieval

  • pgvector
  • Elasticsearch
  • Pinecone
  • Cohere Rerank
  • Unstructured

Models

  • Claude
  • GPT models
  • Gemini
  • Azure OpenAI

App & integration

  • React
  • TypeScript
  • Node.js
  • Microsoft Graph API
  • Slack API
  • Okta

FAQ

Frequently asked questions

Anything else about AI copilots? Ask us directly.

  1. Any language model can produce wrong answers, so we reduce the risk with retrieval from your own content, citations the user can click, instructions to say “I don’t know”, and test sets that catch regressions. The person reviewing the output stays the final check, which is why copilots suit tasks where a quick human read is natural.

Let’s work together

Have a project in mind?

Book a strategy call and we’ll show you exactly how to turn your goals into a system that generates consistent results.

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