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

Transformer-based foundations pre-trained across verticals, heads fine-tuned per client.

The same architectural pattern that made foundation models the default in language is applied here to enterprise behavioral and temporal data — pre-train one backbone self-supervised across many clients, attach task-specific heads to the frozen embedding for each one.

Foundation models

Transformer encoders pre-trained self-supervised across many clients and industries. Two backbone families — a sequence-event transformer over customer / policyholder / subscriber event histories, and a temporal-tensor transformer over multi-SKU multi-channel demand. Both emit reusable embeddings that downstream heads consume.

Backbone family: Sequence-event + temporal-tensor transformers
Pre-training: Self-supervised (masked event modeling, next-event, contrastive)
Training corpus: Multi-client, anonymized, federated where data residency requires
Outputs: Embeddings, sequence representations, calibrated head probabilities
Traceability: MLflow + Bitbucket + model registry · weights, corpora, hashes versioned

Category
Acquisition
Conversion-stage embeddings powering lead scoring and acquisition heads.
Category
Profitability
Multi-product behavior embeddings powering share-of-wallet and revenue-growth heads.
Category
Retention
Pre-churn / pre-attrition embeddings powering survival and retention heads.
Category
Collection
Payment-stress embeddings powering recovery, prioritization, and credit-risk heads.

Downstream heads

Task-specific heads attached to the frozen foundation embedding and fine-tuned for each client's schema, taxonomy, and operational logic. Most heads fine-tune a small fraction of parameters — what training from scratch would need orders of magnitude more data to match.

Task	Model	Note
Advisor attrition	Survival head over ALT embedding	Multi-horizon (90 / 180 day) with calibrated risk.
Product recommendation	Two-tower retrieval over CBT embedding	User × product towers with contrastive training.
Profitability	Regression head + uplift correction	Counterfactual-aware, policy-bounded.
Collection	Cost-sensitive ranking head	Recoverable-amount × channel-cost ranking.

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

heads/advisor_attrition.yaml

prod

name: advisor_attrition_v3
backbone:
  family: advisor_lifecycle_transformer
  ref: alt.v2.1
  frozen: true
  embedding_dim: 192
inputs:
  sequence:
    - window: 24mo
      tokens: [production, manager_touch, book_drift, role_event]
  static:
    - tenure_months
    - region_band
head:
  type: multi_horizon_survival
  horizons: [90d, 180d]
  calibration: isotonic
training:
  regime: linear_probe + head_finetune
  lr_backbone: 0
  lr_head: 1e-4
explainability:
  - shap.head
  - attention_rollout
  - counterfactual
evaluation:
  c_index: 0.81
  auc_180d: 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

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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

Transformer-based foundations pre-trained across verticals, heads fine-tuned per client.

Foundation models

Acquisition

Profitability

Retention

Collection