Platform
Architecture for enterprise AI.
Fligoo SharpAI is a four-layer system covering logical architecture, physical deployment, integrations, and user interface — engineered to take large enterprises from data lake to live, governed AI.
Concept
The Decision Layer.
Most enterprise AI ships a model and stops. Fligoo ships the system that surrounds it — the layer that turns raw enterprise data into live, explainable decisions inside the channels that already run the business.
01 · Input
Data
Client profile, account, transaction, engagement, behavioral signals — ingested, governed, prepared.
02 · Embeddings
Foundational models
Pre-trained across clients and industries. Acquisition, profitability, retention, collection.
03 · Inference
Downstream models
Fine-tuned to client schema and operating logic. Specific tasks, specific recommendations.
04 · Action
Orchestration
Recommendations routed across CRM, call center, branch, digital channels — with policy, bias, and audit checks.
05 · Outcome
Decision
A specific, explainable action — measured against the line of business it was built to move.
↳ Every recommendation carries its rationale, its top drivers, and its counterfactual — explainability is the layer, not an afterthought.
Four layers, end to end.
The platform separates concerns explicitly so each layer can be hardened, audited, and evolved independently.
01
Layer
Logical architecture
Ingestion, storage, experimentation, execution, and output models. The decision graph that governs how data becomes a recommendation.
- Ingestion
- Storage
- Experimentation
- Execution
- Output
02
Layer
Physical architecture
Cloud infrastructure, security, and monitoring — designed for elastic compute, encrypted storage, and the operational telemetry an enterprise audit team will ask for.
- Cloud
- Security
- Monitoring
03
Layer
Integrations
Platform APIs, deployment tooling, and the connectors that move predictions back into the systems running the business.
- API
- Deployment
- Connectors
04
Layer
User interface
Configurable dashboards, customization layer, artifacts, and data services — so business users can act on the model output directly.
- UI
- Configuration
- Artifacts
- Data services
Modeling approach
Foundational models pre-trained across verticals, downstream models tuned per client.
The same approach that has made foundational models the default in language is applied here to enterprise behavioral data — train once across many clients, fine-tune for one.
Foundational models
Large, supervised models pre-trained on standardized tasks across multiple clients and industries. Outputs become reusable features and meta-signals for downstream models.
- Type
- Supervised + unsupervised
- Training data
- Multi-client, anonymized, canonical schema
- Model class
- XGBoost / LightGBM / Random Forest
- Outputs
- Probabilities, indices, meta-features
- Traceability
- MLflow + Bitbucket + model registry
Category
Acquisition
Customer acquisition and lead-scoring optimization.
Category
Profitability
Maximizing share of wallet and revenue growth.
Category
Retention
Enhancing customer retention and loyalty.
Category
Collection
Advanced credit risk management and debt recovery.
Downstream models
Task-specific models that take foundational outputs as inputs and fine-tune for the client's schema, taxonomy, and operational logic.
| Task | Model | Note |
|---|---|---|
| Advisor attrition | XGBoost / LightGBM | Feature importance for explainability. |
| Product recommendation | LightGBM ranker | LambdaMART objective. |
| Profitability | ElasticNet / XGBoost regressor | Wide tabular data. |
| Collection | Isolation Forest + threshold | Semi-supervised baseline. |
Architecture in production since 2022
The foundational pattern that scales enterprise AI.
Fligoo's platform is built on a single architectural principle: train a layer of foundational models across the canonical schema of an industry, fine-tune downstream heads per client, then orchestrate the rollout into the systems that already run the business. The pattern has shipped across ten verticals and counting — long before the rest of the market converged on it.
Principle 01
Foundational layer
Models pre-trained across many clients in a vertical — learning the common signal in customer behavior, transactions, engagement, and risk. The signal a single institution could never reach on its own data alone.
Principle 02
Downstream specialization
Task-specific heads fine-tuned per client to that client's schema, taxonomy, and operational logic — typically tuning only a small fraction of parameters to match what training from scratch would deliver.
Principle 03
Federated learning
Where data residency or partnership boundaries forbid centralization, foundational models train federatedly — only learnings cross the boundary, never raw data, never features.
Principle 04
Network-aware modeling
Compliance, fraud, and risk problems require cross-record relational signal that isolated record processing misses. The architecture treats network-level features as a primitive, not a retrofit — opening AML and risk programs that single-record approaches struggle with.
Why it wins 01
Multi-tenant by design
The foundational layer is trained across many clients in a vertical, not on a single institution's data. Cross-industry diversity is a structural advantage that single-tenant training cannot replicate, no matter how large the institution.
Why it wins 02
Vertical generalization
The same architectural pattern has shipped across banking, wealth management, insurance, retirement, retail, telecommunications, FMCG, chemical, fast food, and mass media. Reusing the foundational layer dramatically compresses time-to-production for every new client.
Why it wins 03
Compliance through cross-record signal
AML, fraud detection, and risk surveillance need network-level features — relationships, timing, and context that span multiple records. The architecture supports cross-record relational modeling natively, opening compliance programs that record-isolated approaches struggle with.
Production surface
What the system actually does, in production.
Live inference logs and the configuration that drives them — abridged for clarity, but shaped exactly like what the platform emits and ingests at runtime.
fligoo-sharp-ai · inference.log · live
features.advisor_attrition.yaml
prod
name: advisor_attrition_v3
foundational: acquisition.v1.4
features:
- name: tx_count_90d
window: 90d
type: numeric
- name: aum_variance_6mo
window: 6mo
type: numeric
- name: call_volume_30d
window: 30d
type: numeric
- name: product_count
window: 12mo
type: numeric
- name: is_multi_product_client
window: lifetime
type: binary
model:
type: XGBoostClassifier
n_estimators: 200
max_depth: 6
explainability:
- shap.global
- shap.local
- counterfactual
evaluation:
auc: 0.92
lift_at_10pct: 4.2
p95_latency_ms: 47Federated learning
Train across sources without private data ever leaving the client.
When data residency, regulation, or partnership boundaries forbid data movement, foundational models can be trained federatedly — only learnings, never raw data, are shared.
01
Local training
Each environment trains on its own data inside its own perimeter.
02
Privacy-preserving exchange
Only model updates and learnings cross the boundary — never records or features.
03
Aggregated foundational model
A single foundational model benefits from breadth without any party exposing raw data.
Explainability
Every recommendation comes with a reason.
Explainability isn't a layer bolted on at the end — it's embedded in the lifecycle so compliance, audit, and the line of business can all answer the question "why?".
SHAP
Global and local Shapley-value attributions for ranked feature importance and per-instance breakdowns.
LIME
Local surrogate models for individual prediction explanations.
Counterfactual search
Minimal feasible feature changes that would flip the outcome — useful for next-best-action playbooks.
Integrated gradients
Attribution for deep-learning components when foundational models incorporate neural layers.
Guardrails
Reliability, compliance, and domain safety enforced across the lifecycle.
Guardrails operate at four levels — data, model, output, and system — with policy compliance, bias auditing, and full lineage built in.
01
Data
Schema validation, drift detection, PII filtering, and sensitive-attribute checks before data enters training or inference.
02
Model
Fairness adjustments, differential-privacy noise where applicable, and stability checks that flag instability before it reaches production.
03
Output
Toxicity and policy compliance checks on every prediction, with explainability metadata attached to every score.
04
System
Policy engine, audit log, retraining SLA, and rollback readiness — so when something looks wrong, recovery is a known procedure.
Evaluation framework
Five axes — because predictive power alone isn't enough.
Models are scored on five dimensions, with explicit metrics for each. A model that ranks high on AUC but low on operational robustness or trust does not move to production.
01
Predictive power
- AUC / ROC
- F1 / Precision-Recall
- Lift @ 10% / KS
- Δ vs baseline
02
Generalization
- Temporal validation
- Cross-domain test
- Feature noise injection
- Retrain drift index
03
Trust
- SHAP consistency
- Explainability coverage
- Bias audit score
- Rule alignment
04
Business impact
- Retention uplift
- Revenue uplift
- Cost reduction
- Predictive ROI
05
Operational robustness
- Latency @ P95
- Availability / uptime
- Drift detection rate
- Retrain SLA
- Rollback readiness
Stack
Industry-standard tooling, deployed for the enterprise.
Modeling
- XGBoost
- LightGBM
- Random Forest
- ElasticNet
- Isolation Forest
- LambdaMART
Data and features
- Pandas
- Polars
- PyArrow
- Dask
- scikit-learn
- category_encoders
Pipelines and deployment
- AWS S3
- AWS Glue
- Airflow
- Kubernetes
- Docker
- MLflow
- Bitbucket
Explainability and validation
- SHAP
- LIME
- ELI5
- scipy.stats
Ready to make AI a measurable line in your P&L?
We design, deploy, and operate enterprise AI alongside your team.