475Cumulus

Service

In-app copilots embedded in your product

Context assembly from the screen your user is already on — ticket, record, dashboard, or workflow — with permissions enforced server-side before any model call.

Who this is for

Product and platform teams adding assistive AI to an existing UI — draft replies, summarize records, suggest next steps, or answer questions scoped to what the user is viewing — without shipping a generic chatbot in a sidebar.

Problems we solve

Common failure modes when copilot, retrieval, or middleware features are bolted on without an integration plan.

  • Copilot demos that send the whole database to the model because context assembly was never designed
  • Detached chat widgets that ignore product state — users re-explain what is already on screen
  • No audit trail or confirmation gates when the copilot suggests actions that touch customer data

Typical deliverables

  • Context builder that formats product state, selected entities, and permitted fields into the prompt — scoped by RBAC per request
  • UI embedded in existing views — panel, inline assist, or command surface matched to your design system, not a third-party widget
  • Server-side orchestration through LLM middleware — streaming responses, tool calls to your APIs where needed, and logging for support review
  • Human confirmation gates for irreversible actions — sends, updates, deletes — with the same authorization layer as the rest of your app

How we deliver

Your eng team stays on the roadmap. We handle the AI integration layer — scoped sprints, PRs to your repo, and handoff docs so your team can operate what we ship.

We identify the lowest-risk, highest-value workflow boundary in the audit — often a single view where assistive AI saves measurable time. The copilot ships behind a feature flag to internal users first, with eval baselines and support playbooks before tenant rollout. Your team keeps ownership of the repo; we deliver typed, reviewed code and handoff documentation.

  1. Step 1

    Technical audit

    Map your architecture, API boundaries, data flows, and auth model. Identify the lowest-risk, highest-value integration point.

  2. Step 2

    Architecture & prototype

    API contracts, middleware design, and a working proof against your real stack — validated before full build commitment.

  3. Step 3

    Build & deploy

    Production code in your repo. Staging, load testing, and canary rollout behind feature flags — with runbooks for your team.

  4. Step 4

    Operate & expand

    Monitor latency, cost, and output quality. Iterate on evals and prompts, then extend to the next workflow boundary.

Related guides

Deeper technical notes from our resources library.

Common questions

How is this different from adding ChatGPT in an iframe?
An in-app copilot reads context from your product state and enforces your permissions model. It runs through your backend, logs requests for audit, and can invoke your product APIs with confirmation gates — not a generic model UI with no connection to tenant data boundaries.
Do copilots require RAG?
Not always. Many copilots start with context assembly from data already loaded in the UI or fetched via your existing APIs. We add retrieval only when the model needs knowledge that is not already in the request — and assess that tradeoff in the architecture phase.
Can you productionize an existing copilot POC?
Yes. That is a common starting point. We assess what is there, move calls server-side, harden auth and observability, add evals and fallbacks, and get the feature behind proper deployment practices so it survives real traffic and your engineering review bar.

Scope an integration for your stack

Describe the feature you are planning — we will map architecture, effort, rollout strategy, and what production-ready means for your system.

Get an integration plan