Predict · Fligoo SharpAI
SharpAI
The predictive foundation.
Foundational models pre-trained across regulated verticals, with downstream heads tuned to each client's schema and policy. Credit scoring, attrition, propensity, default, churn, profitability — every score carries an explainability surface and lineage.
- Foundational + downstream architecture
- Federated learning where data can't leave
- SHAP / LIME / counterfactual explainability on every output
- MLflow + model registry + dataset lineage
→ Feeds AUTONOMY with the score and the why.
Act · AUTONOMY
AUTONOMY
The autonomous execution layer.
Specialized agents — not a generic assistant — take each score and execute. Open tickets, draft outreach across SMS/email/WhatsApp/voice, route through CRM, escalate on policy threshold, log the audit trail. Multi-agent orchestration where the work crosses functions.
- Scoped agents with allow-listed tools and channels
- Human-in-the-loop thresholds + dry-run modes
- Multi-agent orchestration with handoff + audit
- Per-agent KPIs in business terms, not token counts
→ Feeds FDE with telemetry the engineers tune to.
Deliver · Forward-Deployed Engineering
FDE
The senior engineers who ship it.
Forward-deployed engineers embedded with your team — accountable from scope through production and the operation that follows. They translate business problems into shipped models, govern the data underneath, and stay through ops. No layered teams. No handoffs over the wall.
- Senior engineers, not subcontracted juniors
- Embedded with the client team through production
- Industry-specific track record in regulated environments
- Same team end to end — scope, ship, operate
→ Closes the loop back into SharpAI with new data, new signal, new models.