First production AI capability in 6–8 weeks
Governed SDLC with maker-checker model built in
Dedicated pod live in 30 days
Full IP ownership – no vendor lock-in
6–8 wks
First capability live
90 days
Full production AI
100+
AI engineers on-demand
1993
Delivering since
Transform from SDLC to ADLC. We integrate a delivery loop where humans govern intent and quality, while AI accelerates execution from plan → describe → execute → optimize → commit.
Trusted by SaaS and tech leaders globally
Banking – us
SAAS – AU
SAAS – US
SAAS FINTECH – AU
CLOUD – KR
For most SaaS teams, the gap between “we have a PoC” and “it runs in production” takes 9 months or more. That gap is where roadmaps stall, costs balloon, and competitors pull ahead.
The demo wins the room, then nothing ships. Your AI initiative stalls before it reaches a single user.
PoC graveyard
Your existing team is heads-down on product. There is no capacity left for AI without something else slipping.
Roadmap conflict
Hiring senior AI engineers in the US takes 6–12 months and burns $1M+ in loaded cost. The market is dry.
Talent gap
Legal, security, and compliance block release because no one set up controls upfront. The release never comes.
Governance unknowns
AI Pods are designed to remove the handoff gap. Here is how the math changes:
Each AI Pod runs as a self-contained team aligned to your roadmap. We design the pod around your stack and goal, not a generic template.
Design, train, and deploy machine learning models tailored to your product and data.
AI & ML Engineers
Integrate AI capabilities directly into your existing product stack and user flows.
Product Software Engineers
Transform raw data into clean pipelines and feature stores that power reliable AI.
Data Scientists & Engineers
Maintain audit trails, test coverage, and compliance from day one of every sprint.
Delivery Governance & QA
Automate model deployment, monitoring, cost controls, and CI/CD for AI systems.
MLOps & DevOps Specialists
A single accountable owner who aligns the pod to your roadmap and owns the outcome.
Dedicated Pod Lead
Every pod runs on a governed AI-assisted software delivery lifecycle. AI helps draft requirements, design, code, and tests. Humans review and approve every stage. Fast delivery, audit-ready output – critical for regulated SaaS in fintech, healthtech, and enterprise.
Governed AI-assisted SDLC
AI is the maker. It drafts the user story, test plan, code, and validation script. A human checker reviews and signs off before anything moves forward. You get the speed of AI with the accountability of a human-owned process. Borrowed from banking, applied to AI delivery.
Maker-checker model
Every role in our delivery model has AI built in: Business analysis, Solution design, Software implementation, QA. Humans stay in control of intent and quality. AI does the heavy lifting on execution. This is how we hit the 90-day timeline without cutting corners.
MLOps & DevOps Specialists
Six capabilities a CMC AI Pod ships into production.
Embedded AI that drafts, summarizes, routes, and acts inside your SaaS – without users ever leaving your platform. Shipped with full audit trail and role-based access.
Your project management SaaS gets a copilot that drafts tickets, suggests priorities, and summarises standups.
Real example
Surface accurate, cited answers from internal docs, tickets, customer data, and knowledge bases. RAG architecture built for your data governance requirements.
Your support team asks questions in natural language and gets answers sourced from your docs and ticket history.
Real example
Replace brittle keyword search with understanding-based search across product, support, sales, and ops data. Users find what they mean, not just what they type.
A fintech SaaS replaces its legacy search with a semantic layer that surfaces relevant records 4x faster.
Real example
Agents that handle support triage, ops workflows, and sales outreach without human intervention. Every action is logged, reversible, and compliant.
A SaaS operations team deploys an agent that auto-triages 60% of support tickets and routes the rest with full context.
Real example
Automated approval chains, document processing, and compliance reviews with structured human sign-off. Audit-ready by design, not retrofitted.
A banking SaaS automates 80% of document review while flagging edge cases for human approval in under 30 seconds.
Real example
Cost tracking, drift detection, reliability dashboards, and model health alerting. Your AI does not degrade silently in production.
A SaaS platform gets a live cost-per-query dashboard, model drift alerts, and weekly health reports delivered to the CTO.
Real example
Six capabilities a CMC AI Pod ships into production.
Embedded AI that drafts, summarizes, routes, and acts inside your SaaS – without users ever leaving your platform. Shipped with full audit trail and role-based access.
Your project management SaaS gets a copilot that drafts tickets, suggests priorities, and summarises standups.
Real example
Surface accurate, cited answers from internal docs, tickets, customer data, and knowledge bases. RAG architecture built for your data governance requirements.
Your support team asks questions in natural language and gets answers sourced from your docs and ticket history.
Real example
Replace brittle keyword search with understanding-based search across product, support, sales, and ops data. Users find what they mean, not just what they type.
A fintech SaaS replaces its legacy search with a semantic layer that surfaces relevant records 4x faster.
Real example
Agents that handle support triage, ops workflows, and sales outreach without human intervention. Every action is logged, reversible, and compliant.
A SaaS operations team deploys an agent that auto-triages 60% of support tickets and routes the rest with full context.
Real example
Automated approval chains, document processing, and compliance reviews with structured human sign-off. Audit-ready by design, not retrofitted.
A banking SaaS automates 80% of document review while flagging edge cases for human approval in under 30 seconds.
Real example
Cost tracking, drift detection, reliability dashboards, and model health alerting. Your AI does not degrade silently in production.
A SaaS platform gets a live cost-per-query dashboard, model drift alerts, and weekly health reports delivered to the CTO.
Real example
Ideal for
Not ideal for
We include this filter so every assessment call is worth your time and ours.
We review your current AI initiatives, validate feasibility, prioritize use cases, and define the right pod shape. You get a clear plan before any spend.
STEP 1
AI Risk and ROI Assessment
We staff your pod with the right mix of roles for your stack and goal. The pod lead aligns on roadmap, governance, and success metrics with your team.
Step 2
Pod design and kickoff
The pod ships its first production-ready AI capability inside your environment. Real data, real users, full governance from day one.
Step 3
First production capability
The pod continues to expand use cases, optimize performance, and feed adoption and quality metrics back into your business.
Step 4
Scale and iterate
Short, structured, free. We respond within 24 hours.
30 years of engineering delivery, rebuilt around AI. A partner that ships, not one that advises.
Serving US, AU, and MENA clients since 1993 with a 30,000+ engineer network.
Vietnam-headquartered, globally delivered
Everything built belongs to you. Our job is to leave you with something you can run alone.
Full IP ownership, no lock-in
Live delivery across SaaS, fintech, healthtech, banking, and manufacturing sectors.
Proven AI and modernization work
Audit trails, access management, cost monitoring, and secure deployment from day one.
Built-in governance and compliance
More seniors per pod than typical offshore. You get experience, not just headcount.
Senior-heavy delivery model
No middle layer. You work directly with the pod. Accountable, transparent, fast.
Direct delivery, no brokers
partner
An AI Pod is a small, cross-functional team accountable for shipping production-ready AI. You get one delivery unit that owns design, build, integration, governance, and production readiness end to end.
An AI Pod is a small, cross-functional team accountable for shipping production-ready AI. You get one delivery unit that owns design, build, integration, governance, and production readiness end to end.
Most clients see their first production-grade AI capability within 6 to 8 weeks. A full governed AI workflow is in place within 90 days.
No. Pods plug into your existing product or engineering team. You bring the business context. The pod brings the AI capability.
Yes. CMC AI Pods are platform-agnostic. We work with your cloud provider, your data stack, your CI/CD, and your internal tools. No migration required to start.
Governance is built in from day one. Every pod includes access management, audit trails, cost monitoring, and secure deployment. The maker-checker model preserves human accountability on every change.
You do. Full IP ownership and no vendor lock-in. Our job is to leave you with a system you and your internal team can run alone.
Start with an AI Risk and ROI Assessment. Short, structured, free. We validate feasibility and define the right pod shape before any spend is committed.
30-minute structured review. We will get back within 24 hours to schedule. No obligation, no pitch deck.
By submitting, you agree to be contacted about your inquiry. We never share your data. SOC 2 Type II in progress.
What you get from the assessment
A structured, no-obligation review — not a sales pitch.
30 minutes.
That is all it takes. We come prepared. No wasted time on your calendar.
Response within 24 hours guaranteed.
