Kritama

Kritama is a platform for building digital assistants that need to work reliably in production. It focuses on context management, observability, and predictable behavior, targeting teams who have outgrown simple prompt chaining and need something more structured for customer-facing AI.

The core idea is what they call the Fractal Context Engine. Instead of dumping everything into a context window and hoping the model figures it out, Kritama structures context dynamically. As tasks change, it brings relevant information in and pushes irrelevant context out. This keeps the model focused on what matters for the current step without losing track of the broader conversation.

Observable intelligence is a central theme here. When something goes wrong with an AI assistant, debugging can be painful because the model's reasoning is opaque. Kritama makes that visible. You can inspect what the model saw, reproduce the exact conditions that led to a response, and fix problems systematically rather than tweaking prompts and hoping for the best.

The platform uses smaller, more efficient models rather than defaulting to the largest available. This isn't just about cost, though that's part of it. Smaller models respond faster and, when given properly structured context, can match or exceed what larger models do with messy inputs. The tradeoff is that you need better context management, which is exactly what Kritama provides.

Business logic and policies are defined using HCL and markdown rather than being embedded in prompts. This means your rules live in version-controlled files that engineers can review, test, and update without touching the AI layer directly. It's a separation that makes the system easier to maintain as it grows and easier to audit when you need to explain why the assistant said something.

Kritama is aimed at development teams building AI assistants that need to be accurate, maintainable, and scalable. If you're building an internal tool or a prototype, simpler solutions will likely suffice. But if you're deploying something customer-facing where reliability and debugging matter, the structured approach here starts to make sense.

The platform appears to be in preview mode currently, with access available through a waitlist. There's a calendar link for scheduling a demo with the team, which suggests they're working closely with early users rather than offering self-service onboarding yet. Pricing isn't published, which is typical for products at this stage.