The best platforms for embedding AI copilots inside a SaaS product

Choosing an embedded AI copilot platform

Most comparison content about AI agent platforms is written for a company adding AI to its own operations. Automating an internal process, connecting a few systems, giving the ops team an assistant.

That is a different product category from the one you need.

Putting a copilot inside the software you sell means multi-tenant permissions, per-customer data isolation, guardrails a procurement team will interrogate, and audit trails for actions taken on someone else's data. Your users are your customers' employees. The failure modes are contractual.

This page covers the platforms and frameworks that address that problem, what each is genuinely good at, and how to work out which layer of the stack you actually need.

What makes embedding AI in a product different from using AI internally

An internal agent operates inside one organisation, on data everyone in the room can already see, supervised by the person who set it up. If it does something odd, someone notices within the hour.

An embedded copilot runs inside a product used by hundreds of customer organisations at once, each with their own data, their own users and their own permission model. Nobody at your company is watching any individual session.

That difference produces a set of requirements that internal tooling never has to solve:

Multi-tenant isolation. Customer A's copilot must never surface Customer B's data, including through indirect routes like a shared knowledge base or a cached embedding.

Per-user permissions. The copilot cannot exceed the access of the individual user who invoked it. A support agent asking a question should not get an answer assembled from records their role cannot open.

PII handling. Customer data reaching a model provider is a contractual matter, not a preference. You need masking before the model call and a defensible account of what leaves your infrastructure.

Procurement-grade evidence. Your customers' security teams will ask which models you use, where data is processed, what is retained and how you prevent prompt injection. Those answers need to exist before the first enterprise deal, not after.

Audit trails. When an agent takes an action on a customer's record, somebody will eventually ask what happened and why. That log has to be there.

Human handoff. The copilot has to know when to stop and pass to a person, and that handoff needs to reach the right person with the right context.

None of that appears in a comparison of internal automation tools, which is why those comparisons mislead when you read them for this purpose.

Which platform fits your situation

There are four categories, and they solve different layers of the same stack. They are ordered here by how much arrives already built, starting with the most complete. The mistake teams make is picking one and discovering it covers a third of what they needed.

Managed embedded platforms

Platforms in this category provide the whole layer as a service. Orchestration, memory, guardrails, multi-tenant permissions, PII masking, analytics, integrations and human handoff arrive configured rather than built. strikeUp sits here, alongside other vendors selling an embedded rather than internal-use product.

Where they win: time to production and the security posture. If the goal is a copilot live inside your product this quarter, with procurement-ready answers on day one, assembling the three categories below yourself will take considerably longer.

What they do not cover: the parts that make your product unique. Which workflows matter, what a good outcome looks like, how the experience should feel. No vendor can supply that, and any that claims to is selling something generic.

UI-layer frameworks

CopilotKit is the most established option here. Open source under MIT, with React and Angular SDKs, pre-built but customisable chat components, and hooks that let an agent read application state, call frontend actions and render generative UI. Its AG-UI protocol standardises how a frontend talks to an agent backend, so the backend stays interchangeable across LangGraph, CrewAI, LlamaIndex, Mastra and others. A hosted cloud and enterprise tier sit on top of the free core.

Vercel AI SDK is a free TypeScript toolkit from the Next.js team, with a unified provider API that lets you swap between OpenAI, Anthropic, Google and others by changing a line, plus first-class streaming for React, Svelte, Vue and Angular. Version 6 added a formal agent class, MCP support and tool execution approval.

Where they win: control of the experience. If you have engineering capacity and strong opinions about how the copilot should feel inside your product, these give you the frontend without dictating the rest.

What they do not cover: both solve the interface and the model connection. Orchestration depth, durable state, multi-tenant permissions, PII masking and evaluation remain yours to build. The Vercel AI SDK in particular is best understood as a streaming and tool-calling layer rather than an agent runtime, with persistence treated as an application concern.

Agent frameworks

LangGraph models workflows as graphs with nodes and edges, supporting cycles, conditional routing and durable state through checkpointing. Reaching 1.0 in late 2025, it runs in production at companies including Uber, LinkedIn and Klarna. CrewAI, LlamaIndex and Mastra occupy adjacent ground, with Mastra generally the stronger fit for TypeScript codebases.

Where they win: bespoke logic. If your workflows need genuine branching, retries, checkpoints and human-in-the-loop interrupts, these frameworks have the primitives.

What they do not cover: the product surface and the tenancy model. A framework gives you a way to express agent logic. It does not give you a copilot your customers can use, or the security posture their procurement team will ask about.

Durable execution infrastructure

Temporal, Inngest and Trigger.dev solve reliability. Retries, checkpointing, resumption after a crash, and jobs that run for longer than a serverless function will allow.

Where they win: agents that run unsupervised. If work is triggered by a database change or a schedule rather than a user pressing a button, execution durability stops being optional.

What they do not cover: everything above the runtime. These are correctly understood as a dependency rather than an answer.

Picking between the four

The honest test is what you are short of.

Short of engineering time, buy the managed layer. Short of control over a specific interaction pattern, take a UI framework and build behind it. Short of nothing except a runtime for logic you have already designed, take a framework and a durable execution layer. Short of a clear picture of what customers want the copilot to do, ship something small quickly and find out, because that question is more expensive to answer wrongly than any of the others.

How long does building it yourself take?

Around a fortnight for a prototype. Six months or more for something you can put in front of a paying customer.

The gap is permissions, PII masking, evaluation and security review. None of those appear in the demo, and all of them are required before an enterprise customer will let it near their data.

On cost, building the layer in-house realistically runs £150,000 or more per year, covering two to three senior engineers plus permanent ownership afterwards. Buying it typically runs £25,000 to £75,000 per year depending on scope and usage. The full breakdown is in the build vs buy comparison.

What to check before you choose

Questions worth taking into any vendor conversation, including with us.

Permissions model. How does the copilot inherit the access of the individual user who invoked it? Ask for the mechanism, not the assurance.

Data masking. What is masked before a model call, what is configurable, and what leaves your infrastructure? Get this in writing early, because your customers will ask.

Model portability. Which providers are supported, and what breaks when you switch? Agentic workflows raise switching costs everywhere, because prompts get tuned to one model and downstream steps depend on the last. Portability is worth designing for whichever path you take.

Memory. What persists between sessions, where is it stored, and is it isolated per tenant and per user?

Evaluation. How do you tell whether a change made things better? A platform without an evaluation story leaves you shipping on intuition.

Analytics. Can you see what users ask for, what succeeds and where it fails, at a level that informs the roadmap?

Human handoff. What happens when the copilot cannot complete the task? Where does the user go, and does context travel with them?

Rollout control. Can you release to a subset of customers, monitor, and roll back without a deployment?

Copilots and agents are not the same thing

Worth settling before you compare platforms, because vendors use the words interchangeably and they should not.

A copilot completes multi-step work, but a user has to ask. An agent goes further: it runs on a schedule, or starts automatically when something changes in your product. The difference is what starts the work, and it changes the infrastructure requirements substantially. Unsupervised execution needs retry handling, idempotency and tighter permissions, because nobody is watching to catch a mistake.

Most in-house builds stall at the copilot for exactly this reason. The four levels of agent autonomy covers the distinction in more detail.

How strikeUp handles this

Taking the checklist above in order, since that is the fairest way to assess any option in this category.

Permissions are enforced at the infrastructure layer, so an agent inherits the access of the user who invoked it rather than relying on prompt instructions. PII is masked before the model call. Memory is isolated per tenant and per user. Models are swappable, so a provider change does not become a rebuild. Analytics, evaluation and human handoff come with the layer rather than being added later. Rollout is controlled per customer, so a new agent can go to a subset first and be pulled back without a deployment.

Scheduled agents and agents triggered by a change in your own database are live in the platform, which is the part most in-house builds never reach. See what is included for the detail.

You keep the half that matters: which workflows the agents take over, what a good outcome looks like, and how the experience feels inside your product. Two to three weeks to production is the typical timeline. This is the pattern that shows up most often with B2B SaaS platforms and PE-backed roll-ups.

If what you need is summarisation or a help-centre chatbot, you do not need a platform from anyone, including us. A direct model call with retrieval will do it. The infrastructure question becomes real when agents act inside your product, on your customers' data, within their permissions.

See your product, now with AI

strikeUp will build a working demo on your SaaS. It takes less than 30 minutes. No commitment, no sales pitch. Just your product, with AI.

See your product with AI

Frequently asked questions

What is the difference between an AI copilot and an AI agent? A copilot completes multi-step work when a user asks it to. An agent can start work on its own, either on a schedule or when something changes in your product. What starts the work is the distinction, and it determines how much infrastructure sits underneath.

Can you add AI to a SaaS product without an ML team? Yes, if you buy the infrastructure layer. The skills that matter then are product and domain skills, which your team already has. Building the layer yourself usually does require specialist hires, or a much longer timeline.

What does it cost to build AI agent infrastructure in-house? Realistically £150,000 or more per year, covering two to three senior engineers for six months and permanent ownership afterwards. Buying the layer typically runs £25,000 to £75,000 per year depending on scope.

How do you handle permissions when AI acts on customer data? The copilot inherits the access of the user who invoked it, enforced at the infrastructure layer rather than by prompt instructions. Anything less means an agent can surface records the user could not open themselves, which is the failure mode enterprise procurement asks about first.

Is a white label AI agent platform the same as an embedded one? Not necessarily. White label usually means the branding is yours. Embedded means the copilot runs inside your product, on your data model, within your permission structure. Worth checking which one a vendor means.

What happens when the AI cannot complete the task? It should hand off to a person, with context. A copilot that fails silently, or loops, costs more support time than it saves.

Do we need an agent framework as well as a platform? Usually not. Frameworks and managed platforms solve overlapping problems, and running both adds a layer without adding capability. Frameworks make sense when your workflow logic is genuinely unusual and you want to own it.