Inventiple builds domain-specific AI copilots for B2B SaaS products and enterprise workflows. Not chat windows bolted on. Real in-product assistants that see what users are doing, take scoped actions on their behalf, and convert AI capability into actual outcomes — shipped in 6-10 weeks by senior engineers, fixed price.
Every B2B SaaS company is shipping an AI feature in 2026. Most of them ship the same thing: a chat window in the corner of the UI that does roughly what ChatGPT does, just with the company's logo on it. Users try it twice, find it slightly less useful than the version they already use in another tab, and never come back. The feature stops moving any meaningful metric within 90 days.
The reason this pattern fails is straightforward: chat windows are not workflows. Users don't open a chat window to do their job. They use the product's existing screens, forms, and data tables. A copilot that lives outside that flow is just another tab the user has to context-switch to. It loses the battle for attention immediately.
Off-the-shelf copilots don't know your product. They don't see what view the user is on. They don't know which record is selected. They can't call your APIs with the user's permissions. They have no memory of what the user did yesterday in this product. Every interaction starts from zero context, which means every interaction starts with friction.
Generic copilots have nothing domain-specific to offer. Your customers are doing a specific job — closing sales, processing claims, triaging support tickets, writing legal documents. A copilot trained on the internet isn't an expert in their job. A copilot that's been architected with knowledge of your product, your customers' workflows, and your data is.
Most teams underestimate the engineering required. A demo copilot is two days of work. A production copilot — one that handles real users at real volume with real auth and real data — is 6-10 weeks of senior engineering. The gap between the two is where most copilot initiatives stall.
The result for most teams: a copilot launches with a press release, sees a usage spike for two weeks, then quietly fades. We build copilots that don't.
Every copilot we ship is structured around a single goal: reduce the number of clicks or minutes a user spends on a specific task by a measurable amount. The rest follows.
We start by mapping the user's actual workflow — which screens they're on, which actions they take, where they get stuck. The copilot's surface area is defined by where it can meaningfully help, not by adding a chat window to every page. Sometimes the right interface is a chat panel. Often it's inline suggestions, smart defaults, or autonomous background actions.
The copilot has access to the application state — the current record, the current view, the user's role, recent actions, related data. This context flows in via a typed contract (often an MCP server) that we build alongside the copilot. Generic copilots ask 'what do you need?' Ours already know.
Every capability the copilot can invoke is a typed, audited tool. Per-action authorization. Audit logs of every invocation. Confidence thresholds that route uncertain actions to human-in-the-loop. The copilot can't take an action it wasn't explicitly granted.
We build a labeled regression set with your team — real user scenarios, expected outcomes. Every prompt change, capability addition, or model swap runs against the set before deploying. You see quality movement on real workflows, not arbitrary benchmarks.
Helicone or LangFuse on every copilot invocation. You see which capabilities users invoke, where they abandon, what they refine, where the copilot's confidence is low. The product team has the data to improve the copilot in the next sprint, not next quarter.
Three engagement shapes we run on copilot work.
One copilot embedded in one product workflow (e.g., your CRM, your support tool, your dashboards). Scoped capabilities, in-product context, evaluation harness, basic admin controls. Best for: B2B SaaS adding a focused AI feature to drive a specific metric — conversion, retention, time-to-close.
A copilot that spans 3-5 product surfaces with broader capability surface, custom memory across sessions, admin tooling for capability management, and per-customer prompt tuning. Best for: SaaS shipping AI as a major product line rather than a single feature.
Multi-tenant copilots with per-customer customization, compliance scaffolding (SOC 2, HIPAA), admin governance dashboards, role-based capability scoping, audit-ready telemetry, on-prem or VPC deployment options. Best for: enterprises rolling out copilots across multiple internal tools or customer-facing products.
Fixed-price ranges per engagement type, quoted after discovery.
One product surface, focused capabilities.
Multi-surface, broader capability set.
Multi-tenant, compliance, governance.
Provider-agnostic, designed to evolve with the model landscape.
A chatbot answers questions in a conversation interface. A copilot is embedded inside the product workflow — it sees what the user is doing, has context from the application state, takes actions on behalf of the user, and reduces clicks rather than adding a chat window. GitHub Copilot doesn't ask you what code you want; it watches what you're writing and offers completions. A well-designed copilot disappears into the workflow.
Domain-specific, in-product copilots. Examples we've shipped or are shipping: a sales copilot that summarizes calls and drafts follow-ups inside the CRM, a support copilot that suggests responses based on the customer's history and the knowledge base, a finance copilot that explains anomalies in dashboards in natural language, a healthcare copilot that pre-fills clinical notes from voice transcription, a developer copilot that helps internal engineers query proprietary APIs.
Off-the-shelf AI assistants live outside your product. Users have to copy data into them, copy answers back, and lose all the context your application already has. A custom copilot lives inside your product, reads the application state, respects your auth model, calls your APIs with the user's permissions, and produces actions your product already supports. The difference is the same as 'AI tab in your browser' vs. 'AI is part of your application's UX.' The second one converts.
A single-domain embedded copilot (one product surface, scoped capabilities, basic memory) typically costs $40,000-$70,000 over 6-8 weeks. A multi-feature copilot with broader product access, custom memory, and admin controls ranges $70,000-$120,000 over 8-10 weeks. Enterprise copilots with multi-tenancy, compliance scaffolding, admin governance, and per-customer customization range $120,000-$200,000.
Both. We build copilots that range from pure suggestion (user retains every action) to autonomous (copilot executes within defined guardrails) and everything between. For high-stakes actions, we always wire human-in-the-loop approval — the copilot drafts, the user confirms. Per-action authorization, audit logs, and the ability to revoke copilot capabilities mid-session are standard.
Four hardened layers: (1) every copilot capability has typed input/output schemas that fail fast, (2) groundedness verification when copilots reference data — answers cite the source records, (3) evaluation harness with regression tests for every capability the copilot exposes, (4) per-capability confidence thresholds — when the copilot isn't confident, it asks rather than guesses. These are non-negotiable in our copilots and rare in DIY implementations.
Provider-agnostic. Same model swap layer we use in our other AI engagements — Claude, GPT-5, Gemini, or open-source via vLLM, depending on your latency, cost, and compliance requirements. The copilot's behavior is defined by prompts, tool schemas, and eval criteria, not by which model is behind the API. You can swap providers as the model landscape shifts without re-architecting.
Via an MCP server we build alongside the copilot, or via direct API integration if MCP isn't a fit yet. The MCP approach is preferable because it gives the copilot typed, audited, versioned access to your product's data and actions — and the same MCP server can be reused by other AI features later. We've covered this pattern in detail on our MCP server pillar page.
Three layers of measurement: (1) engineering quality — evaluation harness scores on the regression set, latency, error rates, (2) usage telemetry — which capabilities users actually invoke, abandonment rates, refinement rates, (3) outcome metrics — defined with your team, e.g., time-to-close reduction, ticket-handle-time reduction, conversion lift. A copilot that scores well on (1) and (2) but doesn't move (3) needs a product-design fix, not an AI fix. We help diagnose which is which.
30 days of post-launch support included. Most clients then move to a retainer ($20K-$50K/mo) for continuous improvement — adding capabilities, tuning prompts against real usage data, expanding the eval harness, integrating new product surfaces. Copilots evolve with the product they're embedded in; the retainer keeps them aligned without re-engaging from scratch.