The Platform for Forward Deployed AI Engineers

The forward deployed AI engineer works next to the customer, learns the domain in real time, and ships something that works before the requirements have fully settled. The job demands two speeds at once: fast enough to iterate live in a room with stakeholders, and disciplined enough to leave behind a deployment the customer's own team can own and operate. ChatBotKit is built for exactly this motion. Start in the managed platform, prove the solution against real data and real conversations, then lift the same agent into code and infrastructure as code without rebuilding anything.

Iterate at the Speed of the Conversation

The early days of a deployment are about discovery. You are mapping the customer's data, their tools, their language, and the actual problem underneath the one they described. ChatBotKit lets you do that work in the open, adjusting the agent while the customer watches and reacts.

Live Prototyping in the Managed Platform

Stand up an agent in minutes. Wire in datasets, attach skills, connect integrations, and start testing against real conversations the same afternoon. Change the instructions, swap the model, add a tool, and see the effect immediately. The feedback loop is short enough that the customer becomes a participant in the build rather than an audience for a demo weeks later.

Grounded in the Customer's Knowledge

Drop in documents, sync data sources, and the agent answers from the customer's own material. Datasets and retrieval are handled by the platform, so you spend the engagement refining behavior and coverage instead of standing up a vector store and an ingestion pipeline. When the customer says "it should know about X," X is in the agent before the meeting ends.

Real Tools, Real Actions

Connect the agent to the customer's systems through skills, custom tools, and native MCP integration. Validate the genuinely hard parts - the agent calling the right API with the right arguments, handling auth, respecting limits - during the engagement, on real endpoints, so the production version holds no surprises.

Convert the Prototype into Production

A working prototype is the start of the deliverable. The forward deployed engineer's reputation rests on what the customer's team inherits: code they can read, deploy, and maintain after you leave. ChatBotKit makes the prototype and the production system the same system.

The Same Agent, Now in Code

Everything you configured in the platform is reachable through first-class SDKs for Node.js, React, Next.js, Go, and a CLI. Embed the agent in the customer's product, wrap it in their auth, stream responses into their front-end, and orchestrate multi-step flows from their backend. The behavior you tuned in the room is the behavior you ship, addressed through an API the customer's engineers already understand.

Infrastructure as Code with Terraform

The ChatBotKit Terraform provider turns the agent, its datasets, skills, and integrations into versioned, reviewable infrastructure. Hand the customer a repository instead of a console full of manual settings. Their team plans and applies changes through the workflow they already trust, promotes the agent across staging and production, and audits every change through source control. The deployment becomes part of their stack, not a dependency on your memory.

A Clean Handoff

When the engagement ends, the customer owns a documented, code-defined deployment running on infrastructure that scales with their traffic. Usage reports, configurable limits, regional deployments, and a sandbox for future changes are all part of the platform they inherit. The next iteration - by their team or yours - starts from the same primitives, with no rewrite between the prototype that won the deal and the system in production.

Summary

Forward deployed AI engineering lives in the gap between a fast demo and a durable deployment. ChatBotKit closes that gap. Iterate live in the managed platform to find the solution with the customer, then express the exact same solution in SDK code and Terraform so their team can own it. One platform carries the work from the first prototype to the production system the customer keeps.