What is an AI copilot?

An AI copilot is a conversational assistant embedded in your software that understands what you want and helps you do it. It doesn’t stop at answers. Copilots take context-aware actions—querying data, calling APIs, and wiring results back into your tools—within your permissions. Think of a copilot as a teammate inside your stack: you describe the outcome, it plans the steps, and you approve the final move.

“Copilot” is deliberate language. You remain the pilot and decision-maker. The AI accelerates routine work, proposes next steps, and handles the cross-system busywork so your team can focus on judgment, creativity, and edge cases.

What’s the purpose of using an AI copilot?

Most teams operate across a maze of systems—data warehouses, CRMs, billing tools, internal services. Even simple tasks mean five tabs and ten clicks. Copilots collapse those steps into a single, conversational interaction that can reach across tools and return a ready-to-apply result.

Early “vibe-coding” tools showed how generative AI can draft prototypes quickly. But prototypes aren’t production. Enterprises need outputs that inherit security, governance, and live data schemas. That’s where embedded copilots shine—especially when they’re built into the platform where you already develop and run internal software.

Retool’s Assist tab is an example of this approach: a copilot that generates and edits directly inside the Retool editor, against your actual schemas and org permissions. It respects SSO/RBAC, presents real-time visual diffs for human approval, and deploys with one click—so you keep velocity and trust.

How we built AI-assisted development into Retool

By Gabriella Angiolillo, Allen Kleiner

How do AI copilots work? (under the hood)

Under the hood, a copilot combines language understanding, planning, tool use, and safeguards. Together, these turn intent into reliable action without giving up control.

Understanding the request

The copilot uses a large language model (LLM) to parse your prompt and gather immediate context: who you are, what app or screen you’re on, which resources and shared primitives exist, and what happened earlier in the flow. Grounding responses in this context prevents generic answers.

Planning the steps

Instead of jumping straight to an answer, the copilot sketches a plan. If you ask, “Show high-value customers and their five most recent orders,” the plan might include an aggregate to classify value, a filtered orders query, a join, and a transform to shape rows for display.

Using the right tools

The copilot then executes with tools the platform exposes: database queries, HTTP calls, transforms, even UI edits. In Retool, you can target specific components, create or modify queries, and wire results into the interface. Because it operates inside your app, it composes with your standards—shared components, query library entries, and established patterns.

Applying safeguards

Enterprise copilots don’t commit silently. Retool snapshots state, presents proposed changes as visual diffs, parameterizes writes by default, and keeps actions within the permissions of the user. High-impact operations require explicit confirmation. You get speed without surprises.

Copilots vs chatbots vs agents vs models

The vocabulary around AI can blur together. Crisp definitions help pick the right tool.

LLMs (models): The predictive engine that understands and generates language. Not a copilot by itself.
Chatbots: Q&A layers or scripted flows. Good at answers, limited at taking real actions.
Agents: Autonomous goal-seekers that pursue outcomes over multiple steps on their own loop. Powerful but higher risk without strong constraints.
Copilots: Collaborative assistants that act with the user. Scoped to your environment, context-aware, and approval-driven.

If you want safe, repeatable assistance inside business systems—where consistency, auditability, and human judgment matter—copilots are the sweet spot.

A guide to agentic AI workflows in 2025

By Kent Walters

Agents & workflows

What are companies using copilots for?

Every team has necessary but repetitive work: looking things up, moving data between systems, drafting standard responses, or running the same sequence of clicks. Copilots are a natural fit wherever that pattern appears. Here’s how they show up with more specificity.

Sales and customer success

Sales workflows already produce rich signals—call transcripts, email threads, product usage, CRM history. A sales copilot harvests those signals and turns them into action. After a call, it summarizes key moments, identifies objections, and updates CRM fields you approve. When a rep asks, “What’s blocking this deal?” the copilot aggregates signals across systems (stalled usage, support escalations, unanswered pricing questions) and proposes the next best action.

For renewals, it assembles a health snapshot—license utilization, open issues, ROI highlights—into a deck and drafts the renewal email with cited stats. Crucially, outputs are wired to CRM objects so reps can apply changes in one click and keep data clean.

Signals: product usage trends, open support escalations, email/call transcript highlights, open quotes, renewal date, stakeholder map.
Drafted actions: call summary, objection list, next-best action, renewal deck slide, CRM field updates, follow-up email draft.
Guardrails: update only allowed fields, require approval for discounts > X%, enforce territory visibility.

Customer support

Support is speed, policy, and empathy—at once. A support copilot pulls context from billing, subscriptions, logs, and your knowledge base, then proposes a resolution path. If a customer says, “I was overbilled,” it reconciles invoice line items with usage spikes, checks eligibility (discounts, SLAs), and drafts a response that cites relevant policy.

For common fixes, it can execute a safe runbook—issuing a partial credit within thresholds, updating a subscription tier, or tagging the ticket with the right taxonomy—always with human approval. The result is faster time-to-resolution and more consistent application of policy.

Signals: invoice lines vs usage spikes, subscription tier/entitlements, SLA status, past fixes, knowledge base excerpts.
Drafted actions: refund/credit recommendation, policy-cited reply, ticket tags, escalation template, runbook step list.
Guardrails: credit thresholds, mandatory supervisor approval above limit, PII redaction, read-only access on restricted tables.

Finance and analytics

Analysts translate business questions into queries, shape the result, and package it for stakeholders. A finance copilot shortens that loop. If a leader asks, “How did Q3 pipeline conversion compare to last year, by segment?” the copilot runs the warehouse queries, returns a chart and tables, and drafts a short narrative explaining deltas. It can also generate canonical SQL and attach it to the deck so the analysis is reproducible.

For operational chores, it reconciles ledger entries against bank statements, flags variances outside policy, and prepares journal entries for review.

Signals: warehouse facts by segment, prior-year comps, budget vs actuals, anomaly flags.
Drafted actions: SQL query, chart/table package, variance narrative, journal entry draft.
Guardrails: SOX-sensitive actions require review, locked dimensions/definitions, rounding/format rules.

Operations and IT

Internal operations run on cross-tool checklists. An ops copilot turns those into guided, safe flows. A manager can say, “Create a new partner account for ACME with standard access,” and the copilot executes across CRM, billing, entitlements, and support—using the manager’s role and templates.

For IT, the copilot can rotate a credential, restart a service, or onboard a new employee by creating accounts, mailing lists, and permissions. Each step is visible, logged, and confirmable.

Signals: account status, entitlement templates, onboarding checklist, service health, access requests.
Drafted actions: account provisioning steps, ticket creation, service restart/rollback commands, status update.
Guardrails: role-based execution, change windows, mandatory confirmation for destructive ops, audit logging.

Product and engineering

Builders use copilots to move faster in tools and code. Retool can generate a functional UI scaffold, CRUD queries, and transforms from a sentence, then refine everything as you select components or @mention queries to scope the change. “Add a date range filter and a CSV export” doesn’t just place UI; it threads the filter through the query and wires an export button with handlers.

For maintenance, agentic debugging detects brittle logic, proposes a safer pattern, and shows changes as a visual diff you accept line by line.

Signals: affected components and queries, error logs, performance metrics, usage analytics, test failures.
Drafted actions: UI scaffold, CRUD queries, transforms, refactor proposal, test stubs, migration script.
Guardrails: changes behind diffs and approvals, protected resources read-only, feature flags/test env only, linting and code standards enforced.

Compliance and risk

Risk teams can embed a copilot in their reviews. For contracts, it extracts key clauses, compares them to policy, flags deviations, and drafts fallback language. In financial risk, it surfaces anomalous transactions based on historical patterns, suggests investigative paths, and pre-fills a case record with relevant artifacts. Even when the AI never auto-approves, collapsing research into a single, well-sourced summary saves hours.

Signals: clause extractions vs policy matrix, transaction anomalies, user access logs, prior incidents, regulatory thresholds.
Drafted actions: deviation summary, fallback clause language, investigation checklist, case file pre-fill, escalation packet.
Guardrails: mandatory human review, PII masking and retention policies, dual-approval for sanctions/refunds, immutable audit trail.

What are the benefits of AI copilots?

Copilots first show up as speed, then as consistency.

Fewer hops. Replace multi-tool workflows with one request and a reviewed result.
Faster cycles. Get high-quality first drafts—queries, UIs, analyses, or emails—within minutes, not days.
Fewer mistakes. Parameterized writes, confirmations, and standard templates reduce common errors.
Consistent UX. A single conversational layer over many systems makes complex work feel uniform.
Broader leverage. Non-experts use powerful systems via plain language; experts spend more time on high-value tasks.

How to measure copilot performance

A practical way to track value is to baseline “time to complete” for a few target workflows, then measure reduction after the copilot launch. Pair that with a quality metric (e.g., percent of drafts accepted with zero edits) to ensure speed doesn’t erode outcomes.

Does Retool have AI?

Retool has AI built into the platform so teams can design, ship, and operate internal apps with an assistive, conversational layer—without leaving the editor or compromising on governance.

Customers can:

Create intelligent UI with prompt-powered components. Add AI chat and generation blocks directly into your apps so end‑users can ask questions, draft content, or trigger actions via natural language.
Build app logic that integrates LLMs, APIs, and databases. Orchestrate LLM calls alongside SQL, REST, and GraphQL in queries and Workflows to turn prompts into deterministic actions.
Persist context and memory across interactions. Maintain user/session state, prior steps, and domain context so the copilot stays grounded and personalized.
Control inputs, validation, and data handling—all within guardrails. Enforce RBAC, parameterized queries, and review gates; log everything for audit, and require confirmations for high‑impact actions.

Can you build your own AI copilot?

Yes—and with Retool, you can go from idea to a fully functional app using our platform with AI built in where you already build and ship apps. You can connect to your CRM, databases, and APIs in minutes, then layer conversational AI on top that understands your specific workflows and permissions.

Describe outcomes in plain language, wire live data and policies, and ship a copilot that operates safely inside your stack.

Prompt to build faster

The Assist tab in the Retool editor is a conversational pane that turns prompts into proposed app changes for you to review. To give end-users a copilot, you can add AI components or Agents directly into your app.

Ask Retool to scaffold a UI, write or modify queries, or refactor app logic. Select components or @mention queries to scope edits precisely. And no changes go live until you approve and deploy.

A typical build flow looks like this:

Prompt Retool to create the core interface (e.g., a two‑column view with details and AI suggestions).
Connect resources (Postgres, Databricks, Salesforce, Zendesk) and let Retool thread those into queries and transforms.
Ground prompts with the minimum fields needed (e.g., customer_tier, outstanding_balance) and add validations/confirmations for risky actions.
Iterate in place: ask Retool to add filters, exports, or runbooks; accept diffs, ship to a test environment, and promote with one click.

Why build copilots with Retool?

Retool gives you the building blocks to ship copilots that are fast, safe, and integrated from day one.

Context‑aware copilots that understand your apps

Retool supports persisting per-user context in your own data store so apps can personalize across sessions. Apps are LLM‑readable, so the copilot can target specific components and queries without collateral changes.

Secure, permission-aligned & fully integrated

Connect to APIs, databases, and internal systems securely with SSO/RBAC and audit logs. The copilot inherits the signed-in user’s permissions, so writes are parameterized by default, and everything is auditable.

Composable & scalable building blocks

Blend UI, SQL, JavaScript, and AI to build real apps that scale across teams. Use deterministic tools first (SQL, REST, JS) and layer AI where it adds leverage. Versioning, environments, and monitoring help you move from pilot to production confidently.

What’s next?

Copilots aren’t a trend—they’re a new way of interfacing with work. Instead of learning every system, people state intent and review a safe, ready‑to‑apply result. That shift compresses cycles, reduces errors, and makes complex operations accessible to more of the organization.

Retool is designed for this future: AI where you already build, grounded in live data, and governed from day one. Teams can design, deploy, and iterate rapidly—going from a sentence to a shipped, production‑ready app in the same canvas. Our approach centers on approval‑driven workflows, so teams get AI speed with enterprise guardrails.