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AI Services · Forward-Deployed Engineering

Senior AI engineers, embedded with your team — through production.

Fligoo AI Services is delivered through Forward-Deployed Engineering: senior engineers who sit with your team, translate your business into shipped models, and stay accountable through deployment and ongoing operation. No layered teams. No handoffs.

The model · Forward-Deployed Engineering

Forward-deployed engineers — your strategic and operational AI arm.

Not a vendor handing over a model. Not a consultancy handing over a slide deck. Senior engineers embedded with your team, accountable to a measurable outcome — through implementation, evolution, and operation. Same team end to end.

Learn how Forward-Deployed Engineering works

The six obstacles that stall enterprise AI — and how we remove them.

Most AI initiatives don't fail at the model. They fail upstream, at the data, organization, and infrastructure layers.

  1. 01

    Obstacle

    Unstructured, scattered data

    Information lives in disconnected systems with no consistent structure — making it nearly impossible to train models effectively.

    How we remove it

    We assess, consolidate, and structure data into a governed foundation suitable for AI.

  2. 02

    Obstacle

    Disconnected data integration

    Inconsistencies, duplication, and missing governance erode trust in model outputs.

    How we remove it

    We unify sources through a controlled pipeline with quality, lineage, and discard rules built in.

  3. 03

    Obstacle

    No clear AI strategy

    Without a roadmap tied to business goals, AI projects stay isolated and never translate into outcomes.

    How we remove it

    We define where AI moves the number and align each model to a measurable line in the P&L.

  4. 04

    Obstacle

    Scarce specialist talent

    Building internal teams in ML, MLOps, and data engineering is expensive and slow.

    How we remove it

    We extend the client team with specialists who have shipped production AI in regulated industries.

  5. 05

    Obstacle

    Infrastructure and scale

    Most environments aren't equipped to deploy, monitor, and retrain AI continuously at scale.

    How we remove it

    We design cloud architecture for elastic compute, secure storage, and CI/CD across the model lifecycle.

  6. 06

    Obstacle

    Manual, repetitive process

    Even with good models, manual handoffs cap throughput and prevent consistent results.

    How we remove it

    We automate the data and decisioning loop end-to-end — from ingestion to action in production channels.

What we deliver

Six service categories, designed to compose.

Most engagements combine several of these. The shape changes with the maturity of the client's data and AI stack — we meet you where you are.

How we engage

Three modes — picked to match the work, not the other way around.

Engagement structure is a means, not an end. We move between modes as the work moves between phases.

  • Strategic advisory

    Where AI is still being scoped. We define use cases, sequence priorities, design the architecture, and produce the AI roadmap.

    Best for

    AI strategy, architecture review, governance design

  • Embedded specialists

    Where execution is the bottleneck. We extend your team with senior data scientists, ML engineers, data engineers, and MLOps — knowledge transfers as we ship.

    Best for

    Capacity expansion, mentoring, accelerating live programs

  • End-to-end project

    Where the outcome is defined. We own the delivery — defined scope, defined timeline, defined deliverable — with the client team validating at each stage.

    Best for

    Time-to-production, fixed-scope solutions, regulated rollouts

From data to AI

A six-step process tied to business outcomes.

  1. 01

    Assessment and diagnosis

    Business goals, data quality, and the highest-impact opportunities for AI surfaced and prioritized.

  2. 02

    Data lake creation

    Centralize and structure data into a single, governed repository — accessible, accurate, and ready to use.

  3. 03

    Architecture and storage

    Cloud infrastructure designed for scale, with secure storage and regulatory compliance built in.

  4. 04

    Governance and security

    Access controls, data catalogs, and a governance framework for ongoing, safe operation.

  5. 05

    System integration

    Models connected to the platforms that already run the business — real-time data, frictionless flows.

  6. 06

    Modeling and deployment

    Train, deploy, and continuously refine machine learning and generative AI models in production.

The Fligoo SharpAI suite.

Four products built to interoperate — covering predictive AI, omnichannel execution, generative AI, and the data plumbing that makes it all production-grade.

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

Built for enterprise scrutiny.

Foundational + downstream models

Pre-trained foundational models adapted to client schema and taxonomy through downstream fine-tuning.

Federated learning

Models trained across data sources without private data ever leaving the client environment.

Explainability built in

SHAP, LIME, and counterfactual analysis so every recommendation comes with a reason — and an audit trail.

Guardrails by default

Schema validation, drift detection, fairness checks, and policy compliance enforced across the lifecycle.

Implementation

From kickoff to production in 60–90 days.

A structured deployment path designed for regulated environments — security-first, with observable progress at every stage.

  1. Phase 01

    Security and access

    VPN tunnels or secure connection gateways, end-to-end encryption in transit and at rest, role-based access, and SOC 2 / GDPR alignment with the client's policy framework.

  2. Phase 02

    Data integration

    Plug-and-play connectors to CRMs and data platforms, SFTP for structured file uploads, read-only direct database access, and cloud bucket ingestion — whichever combination fits the environment.

  3. Phase 03

    Data selection and modeling

    Pre-defined data maps for financial services accelerate feature selection across client profile, account-level, engagement, product usage, and behavioral signals.

  4. Phase 04

    Production rollout

    Models containerized and deployed via Kubernetes; outputs routed to CRM, core banking, call center, and digital channels through a controlled, auditable pipeline.

What you can achieve

Five outcome categories that translate to a measurable line in the P&L.

Every engagement attaches to one of these. We don't ship models that aren't tied to one.

  • 01

    Anticipate trends with predictions

    Demand, churn, default, lead conversion, attrition — quantified months in advance, with the recommended action attached.

  • 02

    Optimize operations and automate

    Reduce operational time and cost across data analysis, document handling, decisioning, and channel routing.

  • 03

    Personalize and grow with GenAI

    Conversational interfaces, AI-assisted content, and recommendation engines tuned to each customer profile.

  • 04

    Drive sales and loyalty

    Up-sell and cross-sell scoring, next-best-action playbooks, and channel optimization that compound across the funnel.

  • 05

    Centralize and visualize in real time

    Dashboards and decision surfaces consolidating the data that matters — with explainable AI signals embedded.

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

We design, deploy, and operate enterprise AI alongside your team.

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AI Services — Fligoo AI Services