Start With Data That's Actually Ready
Most agentic AI projects stall because the data wasn't ready when the build started. We assess data and AWS platform readiness before a single agent gets built.
Enterprise AI
Put AI Agents to Work: Safely, Efficiently and at Scale
Build agentic AI systems that connect to your data, applications and workflows, while keeping token costs, security, governance and human oversight under control.
The Reality
Everyone Wants Agents. No One Can Prove the Return.
The demo is the easy part. The gap between a pilot and an agent that runs every day is where budgets disappear.
Every function has an idea
With no way to rank use cases by value, feasibility and risk, budget scatters across pilots and the board sees no ROI.
It works in a demo, not in production
Reliability, monitoring and the last mile of engineering surface late, and nobody scoped what daily operation takes.
The data isn't ready, so the agent isn't
Agents are only as good as the data they reach. Scattered or messy information makes them underperform, and trust collapses fast.
Nobody knows if the cloud will hold
Gaps in architecture, integration and pipelines stay invisible until they blow the timeline mid-build.
AI can't act without guardrails
Autonomy without boundaries, human oversight and compliance is a risk a regulated business can't take.
One agent runs fine, ten become chaos
At enterprise scale governance, security and cost multiply, and spend quietly runs away.
Key Benefits
Autonomy Within Defined Boundaries, Not Just a Better Chatbot
From identifying the right processes to designing, building and operating production-ready agents, Flentas helps enterprises move beyond AI experiments to measurable business outcomes.
Governance Built In From Day One, Not Bolted On
Roughly one in five enterprises has mature agent governance today. We wire guardrails and human-in-the-loop from day one — not after the first incident.
A Target Metric Before You Build, Not After
A pilot that cannot be measured cannot justify scale. We agree the pilot charter — scope, success criteria, guardrails, definition of done — before development starts.
A Feedback Loop That Keeps Improving the Agent
Evaluation gates, observability, and continuous improvement feed the roadmap after go-live, so the agent doesn't stop learning the day it ships.
Proof Points
Why Production Discipline Matters
~95%Of generative AI pilots deliver no measurable P&L impact
35%Expected agentic AI adoption in two years, vs. 72% for traditional AI over eight
40%+Of agentic AI projects projected to be cancelled by end of 2027
How It Works
The Flentas Agentic Delivery Model: Assessment to Production to Support
A staged path from the first use case to enterprise scale. Prove value on one process, then extend the platform and the pattern, and stay for the run state.
- 1
Assessment: Find the Use Case Worth Proving
Fixed scope and time boxed, weeks rather than quarters. Process discovery across functions with the teams who run them, prioritized on value and feasibility. Data and AWS platform readiness assessed, and the business case and target metric built.
- 2
Pilot: Prove Value on One Process, in Production
A pilot that cannot be measured cannot justify scale. The pilot charter is agreed — scope, success criteria, guardrails, definition of done. The production-ready agent is built on real data, with guardrails and human-in-the-loop wired from day one, evaluated against golden datasets and fixed test suites.
- 3
Production: Industrialize and Scale
From the scoped pilot to full process coverage, wave by wave, with staged rollout and rollback paths. AgentOps brings versioned releases, CI/CD, and evaluation gates; observability adds tracing, quality dashboards, and drift alerts; cost and governance controls enforce spend.
- 4
Support: 24x7 Operations for Production-Grade Workloads
One partner across the lifecycle, from first assessment to steady state. 24x7 monitoring, incident response, and defined SLAs; infrastructure management; agent and model operations including evaluation reruns; governance and compliance upkeep; continuous improvement that feeds the roadmap.
Technology Stack
Technologies & Tools We Use
Channels
Internal copilots, customer applications, and APIs.
Orchestration & Tools
Strands Agents, MCP, A2A, custom tools and knowledge bases.
Agent Runtime & Services
Amazon Bedrock AgentCore: Runtime, Memory, Gateway, Identity, Observability.
Models & Inference
Amazon Bedrock, embeddings, and guardrails — Claude, Nova, GPT, Gemini, Llama, and Mistral. AgentCore is model- and framework-agnostic.
Data Foundation
S3 data lake, OpenSearch and vector store, and governance — plus hybrid retrieval, GraphRAG, and structure-aware ingestion for tables and scans.
Production Discipline
Golden-dataset evaluation gates, Bedrock Guardrails, AgentCore Identity, least-privilege IAM, VPC isolation, KMS encryption, and CloudTrail audit.
Case Studies
View all client success storiesWhere Agentic AI Makes a Difference
Finance & Back Office
High-volume, structured, measurable work — invoice processing, reconciliation, and reporting are strong first candidates when the data is accessible and reasonably clean.
NBFC
KYCIntelligent Document Processing
A financial services firm modernized a manual KYC process using OCR and Amazon Bedrock to structure extracted fields with confidence scores, improving speed, accuracy, and compliance.
Operations & Supply Chain
Repetitive, bounded-scope processes with a human in the loop for edge cases, and a metric the business already tracks.
FMCG
WhatsAppGenerative AI Vendor Portal & Chatbot
A large FMCG enterprise automated vendor engagement over WhatsApp with context-aware, GenAI-powered responses, integrated in real time with backend procurement systems on AWS.
Customer & Service Operations
High-volume interactions with clear, agreed success criteria — where an agent that reads context and acts beats a chatbot that only answers from a fixed knowledge base.
Engineering & IT
Structured, measurable workflows where agents read context, retrieve live data, decide, and act — with escalation logic handing off to a person only where it matters.
Enterprise Security
Multi-AgentAI-Powered Threat Intelligence
A multi-agent system gives SOC analysts natural-language threat detection and incident investigation, routing queries to TextRAG or GraphRAG specialists over enterprise security data.
“We had a pilot that worked in a demo and nothing that told us if it was working in production. Flentas rebuilt it against golden datasets and fixed test suites, wired guardrails and human-in-the-loop before it touched real customers, and gave us a dashboard that flagged drift before our users did. Eleven weeks later we had the first agent we'd trust to scale, and a target metric that actually meant something to the board.”
VP of TechnologyEnterprise Security Platform, India
What's Next
Where This Fits in Your Journey
One engagement is one stage. Here is what usually comes before and after, so the next step is always clear.
AI Readiness Assessment
A scored roadmap for which AI use case to build first and what it needs.
Explore AI Readiness AssessmentAgentic AI Solutions
Build and run production agents with governance and cost under control.
AI Security Assessment
Guardrails and evidence for the AI you are putting into production.
Explore AI Security AssessmentAgentic Cloud Operations
Around-the-clock operations that keep the estate stable while the plan matures.
Explore Agentic Cloud OperationsGet Started
Move Your First Use Case From Assessment to Production
The organizations that get real value from agentic AI aren't the ones that moved fastest. They're the ones that treated it as a production system from the start, with the discipline to prove it in weeks and scale it without starting over.
- AWS Advanced Consulting Partner
- AWS Managed Service Provider
- 96.5% Client Retention