AI & Automation
AI that works in production. Not in demos.
We build AI systems that operate under real conditions — with real data, real constraints, and real accountability.
2,400+
messages classified daily
94%
classification accuracy
23%
fully automated decisions
0
unaccounted decisions
Watch how we think.
Paste any message. See the reasoning chain.
Most importantly: see where we stop and ask for human judgment.
Or try a sample:
In production:
2,400
messages/day
23%
fully automated
0%
wrong decisions
Enter a message and click Analyze
to see the reasoning chain
Our approach
Six steps. No shortcuts.
Every AI engagement follows the same disciplined process. Click a step to see the detail.
Capabilities
What we build.
Select a domain to see what it solves, what makes it hard, and how we approach it.
Natural Language Processing
Classification, extraction, summarization. Production systems processing thousands of messages daily.
Decision Automation
Rule engines that graduate to ML. Confidence scoring. Human-in-the-loop escalation paths.
Data Pipeline Intelligence
Anomaly detection in data flows. Automated quality checks. Schema evolution handling.
Predictive Systems
Demand forecasting, resource planning, preventive maintenance. Models that update themselves.
Document Understanding
Invoice processing, contract analysis, compliance checking. Structured extraction from unstructured documents.
Integration & Orchestration
Connecting AI components with existing enterprise systems. API design for model serving. Fallback architectures.
Select a capability to explore
See the depth behind each domain
Trust boundary
Where to draw the line.
Drag the trust boundary. Watch the system reconfigure.
Ingest
Auto
Classify
Auto
Route
Auto
Decide
Human
Act
Human
Monitor
Auto
Balanced automation.
Human judgment where it matters. Automation where it is safe.
Typical Problems
- Models that perform well in testing but fail on real data
- Automation that handles common cases but breaks on exceptions
- Unclear accountability when automated decisions go wrong
- Drift in model performance over time without detection
- Integration complexity between AI components and existing systems
Decisions We Make
- Define explicit boundaries for what AI should and should not decide
- Build confidence scoring into every automated decision
- Design human escalation paths before automation paths
- Monitor for distribution shift, not just accuracy
- Version models and data together, not separately
Constraints That Matter
- Explainability requirements in regulated industries
- Latency budgets that limit model complexity
- Data privacy restrictions on training and inference
- Human oversight requirements for high-stakes decisions
- Fallback requirements when AI components fail
What Breaks When Ignored
- Automated systems make decisions no one can explain
- Edge cases cause cascading failures
- Model updates break downstream systems
- No one knows when AI performance degrades
- Regulatory audits reveal undocumented decision logic
Ready to build AI that actually works?
We don't do proof-of-concepts that never ship. We build AI systems that run in production.