CONTROLLED SOURCE DESK
Start with disciplined intake, not an undifferentiated feed.
The source layer is designed to hold approved public-sector inputs, normalize records and preserve where each material fact came from before any fit reasoning begins.
- Approved-source registry
- Source normalization and classification
- Duplicate and version awareness
- Provenance retained with the record
- Freshness state carried forward
Architecture staged. Live source integrations are not represented on this page.
STAGED-NOTICE REVIEW
Move every notice through explicit review states.
Instead of treating every discovered record as a recommendation, Kellette separates intake, classification, fit screening and review so low-quality signals can stop before they consume pursuit time.
- Intake and normalization
- Initial cyber classification
- Company-fit screening
- Review, hold and rejection states
- Escalation when evidence is incomplete
Designed workflow. Customer-facing delivery remains gated by evidence and QA controls.
COMPANY CONTEXT
Fit belongs to the company, not the opportunity alone.
Kellette's company model is intended to turn capabilities, geography, preferences, credentials and explicit exclusions into context for every review decision.
- Core cybersecurity capabilities
- Public-sector and geography focus
- Credentials and contract context
- Explicit exclusions and non-target work
- Feedback from save, watch and dismiss decisions
Explore Company context ↗
REJECTION INTELLIGENCE
A disciplined no is part of the intelligence.
Kellette preserves why an opportunity was removed instead of quietly hiding it. The reason can be inspected, corrected and eventually used to sharpen future screening.
- Low-fit signal elimination
- Reason retained with the decision
- False-positive reduction
- Customer correction loop
- Attention protected before pursuit begins
See the reasoning model ↓
FRESHNESS + EVIDENCE SIGNALS
Evidence can be correct and still be too old to trust blindly.
The evidence layer is designed to make recency, provenance, changed source material and unresolved facts visible rather than collapsing them into a single confidence score.
- Source timestamp and collection time
- Freshness and changed-record signals
- Observed versus derived facts
- Interpretation clearly labeled
- Unknowns preserved for review
Explore trust architecture ↓
OPERATING THROUGHPUT + RESPONSE TIME
Intelligence operations need their own health signals.
Kellette's operating layer is being structured to expose queue state, review velocity and response-time health without presenting invented production telemetry before real customer traffic exists.
- Queue-state visibility
- Processing and review timing
- Exception and escalation rates
- Source-to-brief workflow health
- Cost and model-run metadata
Analytics surfaces are pre-launch. No production throughput claims are shown.
PRIVATE LAUNCH-READINESS SAFEGUARDS
Delivery should stop when the evidence does not clear the gate.
Kellette's launch model uses private QA stops, exception escalation and team review controls so routine work can automate while consequential decisions remain gated.
- Provenance and material-field QA stop
- Tenant and customer-data boundaries
- Human escalation for high-risk decisions
- Private launch gates before delivery
- Audit trail for workflow and approvals
See the evidence boundary ↓