AI That Works in Production. Not Just in Demos.
Production AI Across 4 Industries
18+ Languages Supported
40% Claim Resolution Improvement
AWS AI/ML Certified Team
We design and deploy multi-agent AI systems on Amazon Bedrock AgentCore — production-proven across LegalTech, Healthcare, FinTech, and IoT.
Amazon Web Services
Advanced Tier Services Partner
Why Most AI Projects Stall Before They Matter
Matellio has deployed production AI systems on Amazon Bedrock across LegalTech, Healthcare, FinTech, and IoT. We handle everything that kills AI projects — data pipelines, AWS architecture, security posture, domain modelling, and MLOps — so your AI actually ships.
Most startups have AI ideas stuck in notebooks. The gap isn’t the model — it’s the production engineering around it. We exist to close that gap.
The AI Capability Stack
Six Types of AI.
All Built to Run on AWS.
The Next Phase of AI Is Already Here
Agentic AI on AWS. Three Categories. One Partner With Production Proof.
In December 2025, AWS launched three formal Agentic AI categories within the AI/ML Competency programme. These are not marketing labels. They define AI systems where agents autonomously plan, reason across multiple steps, and take action — completing complex workflows without a human approving every move.
Matellio builds across all three. And we have a production deployment running on Amazon Bedrock AgentCore Runtime.
Agents that complete complex tasks autonomously. Our reference deployment: a trademark risk analysis engine for a US LegalTech company. The agent queries a USPTO database, applies DuPont likelihood-of-confusion analysis across multiple dimensions, and returns a structured legal risk report — replacing attorney-level manual review at every case.
AWS Services
- Bedrock AgentCore Runtime + Langgraph based orchestration
- Bedrock (Claude Sonnet)
- Bedrock Guardrails
- ECS Fargate
- API Gateway
- S3
- ECR
- IAM
- CloudWatch
- CodePipeline
- Auto Scaling
- Route 53
Additional enterprise deployments in progress. Architecture and AWS service stack confirmed across all three categories.
AWS Services
- Bedrock (Claude Sonnet, Haiku)
- Bedrock Knowledge Base
- OpenSearch Service
- Lambda
- API Gateway
- RDS (MySQL)
- S3
- IAM
- EC2
- CloudWatch
- VPC
Outcome metrics (ticket reduction, response time) pending client confirmation. Functional stats confirmed: 18+ languages, 20+ profiles, 4 routing categories, 24/7 availability.
Adapting models to domains where generic LLM performance isn’t enough. Custom ML model training — audio classification, image recognition, gait analysis. Domain-specific data pipelines, hyperparameter tuning, and post-deployment monitoring. Our ML engineering has trained custom models for media AI, industrial inspection, healthcare NLP, and education.
AWS Services
- AWS Glue (data prep)
- S3 (training data)
- SageMaker (aligned architecture)
- CloudWatch
The Numbers. Straight From the Projects.
No Estimates. No Marketing Copy. Just What the Projects Produced.
Four Deployments. Built on AWS. Results Confirmed.
Not Proofs of Concept. Production Systems.
Agentic AI Application
LegalTech
AWS Services
- Bedrock (Claude Sonnet)
- Bedrock AgentCore Runtime
- Bedrock Guardrails
- ECS (Fargate)
- API Gateway
- S3
- AWS IAM
- ECR
- CloudWatch
- CodePipeline
- Route 53
About
A US LegalTech company was manually screening every trademark application for USPTO conflicts — attorney time on every case, no path to scale. Matellio built an autonomous AI agent on Amazon Bedrock AgentCore Runtime that queries the USPTO database, applies the DuPont likelihood-of-confusion rubric, and returns a structured risk assessment (0–10 score, Low/Medium/High/Critical) without human intervention at each step. Bedrock Guardrails on every input and output.
Impact
Consistent DuPont-rubric assessment on every case. Attorney-level review automated. Scalable volume without proportional headcount.
Agentic AI Application
HCM
Workforce Management
AWS Services
- Bedrock (Claude Sonnet, Claude Haiku)
- Bedrock Knowledge Base
- OpenSearch Service
- Lambda
- API Gateway
- RDS (MySQL)
- S3
- IAM
- EC2
- CloudWatch
- VPC
About
A global workforce management company had no self-service query capability — employees raised manual support tickets for information already in the system. Matellio built a production AI chatbot on Amazon Bedrock (Claude Sonnet and Haiku), embedded in the company’s Time and Expense platform. NL-to-SQL against live RDS MySQL. RAG retrieval from 20+ profile-isolated Bedrock Knowledge Bases. Role-based access enforced at query level. 18+ languages auto-detected.
Impact
24/7 self-service HR support. 18+ languages. 20+ company profiles with isolated responses. Support capacity decoupled from headcount growth.
AI for IoT / Edge Intelligence
Consumer IoT
AWS Services
- AWS IoT Core (Device Shadow, OTA firmware),
- Lambda
- ECS
- API Gateway
- S3
- Certificate Manager
- Aurora PostgreSQL
- CodePipeline
- CloudWatch
- Route 53
About
A US IoT hardware company had smart heater hardware ready and supply contracts signed — but no software capability. Matellio built the complete ecosystem from scratch: custom firmware with AWS IoT Core connectivity and OTA updates, iOS and Android apps for scheduling and multi-device control, a web admin panel, and Alexa Skill plus Google Smart Home integration.
Impact
Full product launched on schedule. iOS, Android, Alexa, Google Home — all production-deployed. Client supply contracts secured.
NLP / Foundation Model Customisation
Healthcare
About
A healthcare technology company was spending ~15 minutes per referral letter on manual transcription, formatting, and emailing. Matellio built ClinicalPad: an AI platform using NLP (Named Entity Recognition and POS tagging) to extract structured clinical data from notes and voice dictation, and generate standardised referral letters automatically. GDPR-compliant, with full patient-linked audit trail.
Impact
Referral letter time from ~15 minutes to seconds — confirmed from project documentation.
AI Built for Industries That Can't Afford to Get It Wrong
Domain Knowledge Is the Difference Between a Demo and a Deployment.
Generic AI teams optimise for benchmark accuracy. We optimise for the outcome the business actually needs — which requires knowing the domain, the data, and the compliance constraints before writing a line of code.
Why Matellio for AI/ML on AWS
Beyond Algorithms. Here's What Actually Separates Us.
We Were Running Bedrock AgentCore Before Most Partners Had Read the Documentation
Our LegalTech trademark engine was one of the first production deployments of Amazon Bedrock AgentCore Runtime with LangGraph orchestration. Staying current on AWS's AI roadmap is a deliberate investment - not something that happens accidentally.
Production or Nothing
We don't deliver notebooks. We don't deliver Phase 1 prototypes that need a rebuild before they handle real load. Lambda, ECS Fargate, Bedrock, Aurora - every service choice is made with production characteristics in mind, not demo speed.
Production Quality. India Delivery Economics.
frame the cost advantage explicitly: "AWS-certified AI/ML engineers — Silicon Valley-grade architecture at 40-60% lower delivery cost.
Domain Knowledge Is Non-Negotiable
We've built AI for trademark law, clinical documentation, smart device ecosystems, HR workflow automation, and network operations. The data pipeline, model architecture, and output format for each is fundamentally different. We know this because we've done it, not because we've read about it.
Responsible AI Is in the Architecture, Not the Policy Document
Amazon Bedrock Guardrails on every Bedrock deployment. Structured outputs — risk scores, confidence levels, explainable reports — not free-form AI responses in regulated environments. Human review mechanisms built into every production system. Compliance is not an afterthought. It's a requirement we scope from Sprint 1.
Start With a POC. Scale When It Works.
4-6 week Proof of Concept, scoped and budgeted. No 6-month lock-ins.
AWS AI/ML Certifications
Every Engagement. Certified Engineers. Not Just a Senior Lead.
Matellio’s AWS-certified AI/ML engineering team is India-based — Jaipur and Jodhpur — bringing production-grade AI/ML capability at India engineering economics. Whether you’re building your first Generative AI product on Bedrock, deploying a computer vision pipeline, or modernising a complex multi-service ML environment, every engagement is staffed with engineers who hold the certifications that matter.
AWS Certified Machine Learning Engineer - Associate (MLA-C01)
ML pipeline design and deployment on AWS. SageMaker-aligned architecture, MLOps, production model serving. Core cert for AI/ML Competency.
AWS Certified AI Practitioner (AIP-C01)
Applied AI and GenAI on AWS — Bedrock, SageMaker, AWS AI services. Includes Bedrock AgentCore in scope from March 2026.
AWS Certified AI Practitioner Foundational (AIF-C01)
Foundational AI/ML. Baseline requirement for all engineers on AWS AI/ML engagements at Matellio.
AWS Certified Solutions Architect Professional
Complex, multi-service AWS solution design. Required for AI/ML engagements with multi-account, regulated, or HA requirements.
AWS Certified DevOps Engineer Professional
CI/CD and IaC for AI/ML workloads. ML pipelines built with production-grade deployment practices as standard.
Frequently Asked Questions
What Buyers Actually Want to Know About AI on AWS
What's the difference between Generative AI, Agentic AI, and traditional ML?
Traditional ML: models trained on data to make predictions — classification, anomaly detection, recommendation. Generative AI: LLMs that generate content and reason over context in natural language. Agentic AI: agents that autonomously plan, reason across multiple steps, and take action — completing complex tasks without a human approving every move. Most production AI systems use all three: an LLM for the natural language interface, a trained ML model for structured prediction underneath, and optionally an agent layer for multi-step task completion.
Which AWS services does Matellio use for Generative AI?
Our primary GenAI stack: Amazon Bedrock (Claude Sonnet and Haiku), Bedrock Knowledge Base with OpenSearch Service for RAG, Bedrock Guardrails for responsible AI filtering, and Bedrock AgentCore Runtime for Agentic AI. Lambda for serverless execution, ECS Fargate for containerised agents, RDS or Aurora for production data access.
What is Agentic AI and do you actually build it — or just talk about it?
We build it. Our trademark risk analysis engine for a US LegalTech company runs on Amazon Bedrock AgentCore Runtime — the agent autonomously queries a USPTO database, applies a legal rubric across multiple evaluation dimensions, and returns a structured risk report without a human in the loop at each step. That is Agentic AI in production, not a whiteboard diagram.
Can you build AI for regulated industries?
Healthcare, FinTech, and LegalTech are three of our most active verticals. GDPR-compliant NLP for clinical documentation. HIPAA-eligible workloads on AWS. Structured output requirements and explainability for legal AI. Bedrock Guardrails for input and output filtering. Regulated isn’t a special mode for us — it’s the default for a significant portion of what we deliver.
What size engagements do you take on?
We work with founders building their first AI product through to scale-ups adding AI to a platform already serving hundreds of thousands of users. The qualifier is not size — it’s whether the problem is real, AWS is the right platform, and the business outcome is clear. The free discovery call is the fastest way to find out if there’s a fit.
How much does an AI project on AWS cost?
We scope every project with a free architecture call before quoting.
How long from idea to production?
POC: 4-6 weeks. Production MVP: 8-12 weeks. Full system: 12-20 weeks.
What's your engagement model?
Free scoping call → Paid POC → Scale to production. No 6-month lock-ins.
Do you offer AWS GenAI POC funding support?
Yes. We help qualifying startups access AWS’s GenAI POC funding.
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