Fligoo SharpAI

Our enterprise AI platform.

Fligoo SharpAI is the enterprise AI platform that runs predictive, generative, and agentic workloads in production — covering logical architecture, physical deployment, integrations, and user interface, engineered to take large enterprises from data lake to live, governed AI.

Inside SharpAI

Four tools, one platform.

AUTONOMY runs autonomous agents on top of foundational models. PracticeAI applies predictive intelligence to wealth and advisors. AI Orchestrator runs the omnichannel execution layer. DataMoveX moves and prepares the data underneath. All four share the same governance, audit, and explainability layer.

AUTONOMY

Specialized AI agents that take real action across the enterprise.

Multi-agent platform that fuses Fligoo predictive models with autonomous execution. Agents take action on every score — collections, retention, supply chain, fraud — with full governance, explainability, and audit.

30+ specialized agents across collections, retention, supply chain, fraud
Multi-agent orchestration with policy and audit
Predict → Decide → Act closed loop
Integrated with the systems already running the business

PracticeAI

Enterprise AI for wealth management and financial advisors.

A technology-powered solution that gives advisors and their managers customized, intelligent recommendations to retain and engage investors — driving wallet share, predicting attrition, and protecting AUM.

Investor attrition prediction with months of anticipation
Advisor and broker performance benchmarking
Next best action and personalized offers
Customer loyalty and satisfaction scoring

AI Orchestrator

Omnichannel campaigns triggered by AI insight.

Executes sophisticated multi-channel communication campaigns — across SMS, email, WhatsApp, RCS, and call centers — with channel, sequence, and content selected per customer for sales lift, retention, collections, and engagement.

Sales optimization with personalized content
Attrition reduction via targeted retention plays
Collections maximization with cost-aware contact strategy
Omnichannel routing tuned to each customer profile

DataMoveX

Data movement and preparation for production AI.

Automates data extraction, cleansing, normalization, and integration so that Fligoo models reach production faster — replacing manual, error-prone pipelines with a controlled, secure, and scalable process.

Automated extraction, cleansing, and normalization
Pre-built connectors for leading data platforms
Data quality assessment and lineage tracking
Governance, integrity, and discard rules built in

↳ AUTONOMY is the agent product. Each agent is paired with a foundational SharpAI model. Go deep on AUTONOMY →

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: 47

Federated 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.

Predictive power

AUC / ROC
F1 / Precision-Recall
Lift @ 10% / KS
Δ vs baseline

Generalization

Temporal validation
Cross-domain test
Feature noise injection
Retrain drift index

Trust

SHAP consistency
Explainability coverage
Bias audit score
Rule alignment

Business impact

Retention uplift
Revenue uplift
Cost reduction
Predictive ROI

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.

Start the conversation

Our enterprise AI platform.

Four tools, one platform.

AUTONOMY

PracticeAI

AI Orchestrator

DataMoveX

The Decision Layer.

Data

Foundational models

Downstream models

Orchestration

Decision

Four layers, end to end.

Logical architecture

Physical architecture

Integrations

User interface

Foundational models pre-trained across verticals, downstream models tuned per client.

Foundational models

Acquisition

Profitability

Retention

Collection

Downstream models

The foundational pattern that scales enterprise AI.

Foundational layer

Downstream specialization

Federated learning

Network-aware modeling

Multi-tenant by design

Vertical generalization

Compliance through cross-record signal

What the system actually does, in production.

Train across sources without private data ever leaving the client.

Local training

Privacy-preserving exchange

Aggregated foundational model

Every recommendation comes with a reason.

SHAP

LIME

Counterfactual search

Integrated gradients

Reliability, compliance, and domain safety enforced across the lifecycle.

Data

Model

Output

System

Five axes — because predictive power alone isn't enough.

Predictive power

Generalization

Trust

Business impact

Operational robustness

Industry-standard tooling, deployed for the enterprise.

Modeling

Data and features

Pipelines and deployment

Explainability and validation

Ready to make AI a measurable line in your P&L?