Work With Us

Clear process.
Aligned incentives.
No surprises.

Every engagement starts with a conversation. No pitch decks. No account managers. Engineer to engineer.

How it starts

Four steps to deployment.

01

Strategy Call

30-minute conversation. You describe the challenge. We assess fit and recommend an engagement model. A founder is on every initial call.

02

Discovery Sprint

1-2 weeks. We map requirements, architecture, and engagement structure. Fixed price. You get a full technical assessment and recommended approach.

03

Proposal

Pod structure, milestones, timeline, and investment. ARCHITECT drafts the technical approach. You review, we refine, then align on scope and terms.

04

Kickoff

Engineering team + AI pipeline active Day 1. Metrics dashboard live from Week 1. All six AI systems operational by Week 2.

What to expect

Your first 90 days.

Week 1-2

Codebase onboarding

Architecture review. Environment setup. AI pipeline configuration. Metrics dashboard live. Initial code analysis and dependency mapping.

Week 3-6

First delivery cycles

First features shipped. Code review velocity improving. Test coverage increasing. SENTINEL and SHIELD fully operational. Weekly metrics reports begin.

Week 7-12

Steady-state velocity

All six AI systems at full capacity. Deployment cadence established. Documentation coverage targets met. Pod operating at designed throughput.

Operational boundaries

Clear ownership. No ambiguity.

GyanMatrix owns

  • Engineering execution
  • AI pipeline setup and operation
  • Code quality governance
  • Documentation targets
  • Weekly metrics reporting
  • Deployment safety

You retain

  • Product direction
  • Architecture sign-off
  • Release approval
  • Full IP ownership
  • Dashboard access
  • Direct pod communication

We work best with

01
Decision-maker access

We need direct access to someone who can make product and architecture decisions without committees.

02
Clear problem definition

You know what needs to be built or fixed. We bring the engineering system to execute it.

03
Commitment to AI-native approach

Our SDLC is AI-augmented by design. Teams that embrace this see the fastest results.

04
Responsive feedback cycles

We ship fast. The faster you review and respond, the faster we deliver value.

What you get from us

01
Experienced engineers + AI-augmented SDLC from Day 1

Not juniors with AI tools. Senior engineers with an AI-augmented pipeline that multiplies their output.

02
Weekly metrics dashboard

Real-time visibility into velocity, code quality, test coverage, documentation, and deployment frequency.

03
Named pod with ownership

Your team has names, faces, and accountability. No anonymous offshore bench.

04
Direct founder access

Rajan is involved in every strategic engagement. No layers between you and leadership.

Investment

Transparent pricing.

No hidden fees. No surprise invoices. Every engagement includes AI-augmented SDLC at no additional cost.

Engineering Pods
$15K
per month

Cross-functional team embedded in your workflow. Scales monthly.

Milestone Delivery
$25K
per phase

Fixed-price delivery of defined scope with clear acceptance criteria.

GCC Design + Operate
$50K
design + $20K/month ops

Full GCC setup in India with AI-augmented engineering built in.

Legacy Migration
$10K
assessment + phased execution

Systematic modernization with AI-assisted analysis and rollback safety.

The economic case
42%
Faster code reviews
Daily
Production pushes
$0.12
Per pipeline action
FAQ

Common questions.

How is GyanMatrix different from a traditional offshore development partner? +

GyanMatrix does not operate as a staff augmentation provider. We deliver engineering outcomes through a proprietary AI-augmented SDLC with six named AI systems, governed by the ARC framework. Every engagement includes AI-assisted code review, automated testing, documentation generation, and deployment safety — infrastructure most firms do not offer.

Will AI replace human engineers in your delivery model? +

No. Our approach is human-led and AI-enabled. Experienced engineers make every architectural decision, review every pull request, and own every deployment. AI systems handle repetitive tasks — test generation, documentation, code review pre-screening — so engineers focus on judgment-intensive work.

Can you work with our existing system, or do you require a rewrite? +

We specialize in working with inherited systems. Our Legacy Code Migration model uses AI-assisted code analysis to map dependencies, generate test coverage for untested code, and execute phased modernization without disrupting production. We keep your system running while we modernize it.

How do you onboard into an existing product environment? +

We follow a structured transition. Week 1-2: codebase onboarding, architecture review, environment setup, and initial AI pipeline configuration. Week 3-6: first delivery cycles with metrics dashboard live. By Week 7-12, the pod reaches steady-state velocity with all six AI systems operational.

How do you ensure quality when using AI-assisted development? +

Every stage includes validation by experienced engineers. SENTINEL pre-reviews code for security and standards before human review. SHIELD generates test coverage that engineers verify. GUARDIAN assesses deployment risk before any release. AI accelerates — engineers decide.

What types of organizations do you typically work with? +

We partner with product companies modernizing legacy platforms, startups scaling from MVP to production, and enterprises building or optimizing Global Capability Centers. Common thread: organizations that value engineering discipline and want AI-augmented delivery without sacrificing quality.

Do you integrate with our existing tools and processes? +

Yes. We work within your current ecosystem — your repos, your CI/CD, your project management tools, your communication channels. Our AI systems integrate alongside your existing toolchain rather than replacing it.

How quickly can we expect to see impact? +

Most engagements begin with measurable improvements in development throughput and visibility within the first 60-90 days. The metrics dashboard goes live in Week 1, so you have real-time data on velocity, quality, and deployment frequency from the start.

Are your own products built using this same model? +

Yes. Products such as digri.ai and Veril.ai are developed internally using the identical Engineering OS, AI systems, and governance framework we deploy for clients. Our products are proof that the system works.

What does a typical engagement look like? +

We start with a focused collaboration to define scope, then proceed through a Discovery Sprint to map requirements and architecture. From there, we deliver a proposal with pod structure, milestones, and investment. Kickoff includes full AI pipeline activation from Day 1.

How do you handle security, compliance, and data governance? +

We align with client-defined security policies and compliance requirements. SENTINEL checks for security vulnerabilities in every code review. GUARDIAN includes security verification in every deployment. We support SOC 2, GDPR, HIPAA, and custom compliance frameworks as required.

How do we get started? +

Engagement begins with a working session to understand your system, goals, and constraints. No sales pitch. No account managers. A founder and a senior engineer assess fit and recommend the right engagement model. From there, we scope a pilot or Discovery Sprint.

Get in touch

Start a conversation.

Or reach us directly

we@gyanmatrix.com +1 909-677-2929 +91 91393-93333