One platform.
Idea to production.
AI fails in stages. First it forgets what it knew. Then accuracy drifts. Then something breaks and no one can explain why. Each failure has a different cause. Fixing one doesn't fix the others. Unless they're built as one system.
Complete applications that plug into your product.
Runs in your environment, looks like you built it
The AI application lives inside your infrastructure or ours. Your customers see your product, not a third-party tool.
Containerized. Deploy to AWS, GCP, Azure, on-prem, or our cloud.
Connect to your existing database
Works with Postgres, Supabase, or whatever you already use. No migrating data.
Embed however you need
Iframe, API, or direct integration. Fits your architecture.
Compliant from day one
SOC 2 and HIPAA. Audit trails and access controls built in.
Automatic Optimization
Our agents continuously monitor and improve your app getting you 98% accurate outputs now and in production.
Data preparation included
We clean and structure your data so AI has the right foundation.
AI that remembers what matters.
Most AI hits a wall when context gets too big.
We built a persistent memory layer that sits outside the model's context window. Everything gets captured. Only what's relevant for each request gets retrieved.
Cumulative intent
When users make changes across multiple interactions, the system merges them into one continuously updated picture. No request gets treated in isolation. Prior constraints and dependencies carry forward automatically.
Distilled history
Instead of replaying full conversation logs, the system generates compressed representations that preserve decisions and constraints while stripping noise. The model sees one coherent state, not a sprawling thread.
Hierarchical retrieval
For large codebases or document sets, the system builds maps of how components relate. When it needs something specific, it expands only the relevant branches rather than loading everything into context.
See everything. Control everything.
Black boxes don't survive enterprise procurement.
If you can't explain what it did and why, compliance won't sign off. If you can't roll it back, engineering won't trust it.
Set your AI policies for all apps across your organization to behave the way you or IT wants.
What it captures
Every decision the AI makes gets logged with the reasoning behind it. Every input, every output, every human correction. Full version history so you can see exactly what changed and when.
How governance integrates
Define which actions require human approval before execution. Set role-based access so teams see what they should. Export complete audit logs for compliance review. Roll back any change instantly without filing an engineering ticket.
Quality that improves automatically
AI degrades after launch. Models update. Data shifts. Edge cases accumulate. What worked at launch slowly stops working, usually without anyone noticing until customers complain. We built continuous measurement and optimization into the platform.
Measurement
You define what success looks like for your use case. Every output gets evaluated against those criteria automatically. Results are tracked over time so you see trends, not just point-in-time snapshots.
Drift detection
The system monitors for performance shifts continuously. When accuracy drops or behavior changes, alerts fire before customers notice. You can see exactly what changed and when it started.
Auto-optimization
The system tracks which approaches lead to successful outcomes and which lead to errors. It learns from real production behavior and improves automatically. No manual prompt tuning required.
98%+
accuracy in production