Maximize
Move AI beyond pilots and put it
to work in production.
We build AI agents and generative AI applications that handle real workflows, with guardrails, evaluation and cost controls built in. Then we help you run them.
Trusted by 750+ high-growth and enterprise brands
The problem
AI isn't one problem. It's four.
Any one of them keeps AI stuck in a demo. Each one is fixable.
Pilots that never ship
Many AI pilots stall on data access, permissions and evaluation, so they never reach production.
Unclear value
Use cases chosen for novelty rather than measurable business value are difficult to evaluate.
Runaway costs
Unoptimized model calls, vector stores and infrastructure drive costs up faster than value.
Siloed data
AI is only as good as the data it can reach, and much of that data sits in separate systems.
The path
One partner. Four ways to put AI to work.
From the data underneath to agents that act, each piece is built to reach production and stay there.
- 01 · Solves: siloed data
Data Analytics & ML Foundations
Data lakes, pipelines, governance and analytics in the cloud, with AWS Glue, Amazon Redshift and Amazon QuickSight validations. AWS Data Services Competency.
Data lakes and pipelinesGoverned access - 02 · Solves: unclear value
Applied AI for Business
Computer vision, NLP, predictive analytics, fraud detection and recommendation models, each tied to a business KPI and built on services such as Amazon SageMaker AI.
Amazon SageMaker AIKPI-linked models - 03 · Solves: pilots that never ship
Generative AI Applications
Assistants and copilots grounded in your own data with retrieval-augmented generation (RAG), fine-tuned where it helps, and tested for accuracy and safety before launch.
RAG and fine-tuningAmazon Bedrock Guardrails - 04 · AI that acts
Agentic AI Solutions
AI agents that plan and complete multistep work across your systems, such as investigating incident root causes or classifying and summarizing service tickets. Built on Amazon Bedrock AgentCore with scoped access, guardrails and tracing.
Amazon Bedrock AgentCoreMulti-agent orchestration
Capabilities
Start with your use case.
Pick one use case to start, then build on the same foundation.
- Applied AI projects
- 19+
- Faster image processing
- 14×
- To a working prototype
- 2–3 weeks
- Projects delivered
- 1,000+
From use case to production
Five steps, with real numbers before you scale.
You see accuracy, response time and cost on your own data before committing further.
Discover
Pick the use cases worth doing, tie each to a business KPI, and check funding eligibility.
Design
Choose the model, data sources, guardrails and success measures for each use case.
Prototype
Build a GenAI proof of concept on your data in 2 to 3 weeks, with measured accuracy, response time and cost.
Scale
Harden it for production: security, evaluation, monitoring and cost controls.
Operate
Monitor quality, cost and usage after launch, and keep improving it.
What you get
What every AI engagement
includes.
Governed from day one, on the same secure cloud foundation we migrate, modernize and run.
- Every use case tied to a business KPI
- Funding eligibility checked at the start
- Accuracy, response time and cost tested before launch
- Amazon Bedrock Guardrails for safe, compliant outputs
- Scoped permissions and tenant isolation for agents
- Logging and tracing of agent and model activity
- Encryption, least privilege and private networks
- A choice of models across providers, without dependence on a single model
- Cost controls for model calls and infrastructure
- Monitoring and support after launch
Credentials
AI credentials you can trust.
Independently validated for exactly this kind of work.
AWS AI Services Competency
AWS Data Services Competency
Amazon QuickSight Service Validation
Premier Tier Services
Outcomes
Production results, not pilots.
AI that's live and doing real work for our clients.
Enabling Agentic Automation with Amazon Bedrock AgentCore for a Leading WorkforceTech Company
Employee Experience Platform
Built on
Amazon Bedrock
Generative AI for a HR Technology team
Read the story →
Empowering Shop-Floor Operations with GenAI-Driven Operator Assistance Using AI at the Edge
An IT services company
Built on
Amazon Bedrock
Generative AI for a IT Services team
Read the story →
A Leading Wearable Brand Partners with Comprinno to Drive Marketing Campaigns with Generative AI
A retail company
Built on
Amazon Bedrock
Generative AI for a Retail team
Read the story →
Questions before you
build?
Quick answers to what teams ask us before an AI project.
How is an AI agent different from a chatbot?
Agents plan and carry out tasks across your systems within set permissions. Chatbots mainly answer questions.
Where should we start?
With one frequent, measurable workflow that has accessible data. The AI readiness assessment reviews data, governance and architecture, then delivers a roadmap and business case.
How long does it take to reach production?
A GenAI proof of concept takes 2 to 3 weeks, and the goal is measurable ROI within 12 weeks. Timing depends on the use case and data readiness.
How do you protect our data?
Your prompts and outputs are not used to train the underlying models. Processing stays in your chosen region, with encryption using managed keys, least-privilege access and private network subnets.
How do you keep agents under control?
Agents get only the access they need through scoped roles and temporary credentials. Guardrails filter unsafe output, and every agent and model action is logged and traced.
How do you measure accuracy and limit hallucinations?
We test accuracy, response time and cost per request before launch, ground answers with RAG and source citations, and track production accuracy on dashboards.
Are we locked into one model?
No. We select models from multiple providers to suit each use case.
Can our AI project qualify for funding?
Often, yes. We assess eligibility for relevant funding programs at the start, during the AI readiness assessment. Availability depends on the program, project, geography and provider approval.
Who runs the system after launch?
Comprinno can operate it, or hand it to your team with documentation and knowledge transfer.
Let's talk
Have an AI use case that needs
to reach production?
Start with an AI readiness assessment. You’ll have a prioritized use case, defined success measures and a funding eligibility assessment before development begins.
- Step 1AI readiness assessment
- Step 2Use case, KPI + funding check
- Step 3A working prototype in 2–3 weeks