A Process That Puts You in Control - From Day One.
Every business has different needs. Whether you're starting with an idea, require a fully structured plan, or need ongoing support, we tailor our engagement models to fit you perfectly. Our process is built to ensure clarity, efficiency, and long-term success.
Let’s find the approach that works best for you…
Choose Your Engagement Model
How Do You Want to
Work With Us ?
We have multiple engagement models depending on where you are in your journey. Select the option that best fits your needs, and we’ll guide you through our process.
I have just an idea or reference platform
I need Discovery Phase.
I have clear requirements
I want a Fixed-Price Model.
I prefer an Agile, sprint-based approach
I want an Hourly Model.
I need a dedicated team
I want to hire full-time developers.
I need ongoing support
I want a Maintenance & Support Plan.
How we work
Value-Driven Engineering:
AI Speed, Engineer Accountability
People lead and AI accelerates. Our business analysts (BAs), designers, and engineers work with AI tools and agents, review every output, and make every decision. A written spec, agreed with you before development starts, guides all seven steps, the core of how we practice Value-Driven Engineering.
01 / 07
Strategic AI Discovery
Discovery and scoping
BAs learn your goals, users, and requirements and define the scope with you, using AI tools to analyze documents, map existing systems, and check market trends, so the first spec is accurate.
- Output: Requirements, workflow maps, draft spec
- Human checkpoint: You sign off the scope
02 / 07
Architecture for Outcomes
Solution architecture
BAs plan the features, team, resources, tools, and budget. Engineers then choose the architecture, data model, and integrations to fit your goals and growth.
- Output: Architecture plan, resource plan, budget estimate
- Human checkpoint: Engineers own the estimate
03 / 07
Intelligent Product Design
UX/UI and prototyping
Designers work with AI design agents to explore layouts for every user role, and a BA checks each screen against the spec. Your approved prototype becomes the baseline, and later changes show their time and cost first.
- Output: Approved prototype and spec
- Human checkpoint: You approve the prototype
04 / 07
AI-Powered Build
AI-assisted development
Engineers build in two-week sprints with AI coding agents that turn the spec and designs into code, tests, and documentation. The compliance your product needs is built in as code is written, and you see a working demo every sprint.
- Output: Working software every sprint
- Human checkpoint: An engineer approves every merge
05 / 07
Quality-First Validation & Testing
Code audit, QA, security, and compliance
Every pull request is AI-scored before engineer sign-off. QA engineers combine automated tests with manual testing of security, integrations, and compliance, against the spec's acceptance criteria.
- Output: Test reports and compliance checklist
- Human checkpoint: Your team signs off UAT
06 / 07
AI-Enabled Delivery
Launch and rollout
Automated checks verify migrated data, and engineers review every result before go-live. Rollout fits your operations, your users are trained, and code, documentation, and designs are handed over in full.
- Output: Live software, migration report, handover
- Human checkpoint: Nothing goes live without human review and your approval
07 / 07
Continuous Support & Optimization
Support, monitoring, and growth
Monitoring tools flag errors and performance issues before users notice. The team that built your software ships fixes, keeps it secure, and plans new modules as your needs grow.
- Output: Support plan and improvement roadmap
- Human checkpoint: The team reviews every finding
Every pull request is AI-scored across 9 criteria, then approved by an engineer before it merges.
- Spec-Driven delivery
- A human checkpoint at every step
- Engineer-approved merges
- Full handover
How we engineer
Our AI-Native Engineering Standards
AI-native by practice, engineer-led by principle. These are the rules every SolGuruz team works to, on every project, whatever the stack or industry.
01Architecture and Design Quality
Spec-Driven Requirements
Every feature has written acceptance criteria and a traceable spec before development starts, and AI checks the spec for gaps, conflicts, and missing edge cases.
Engineer-ledEngineers review every spec for feasibility before it’s estimated.
Architecture Standards
Architecture, data model, state management, and integrations follow proven patterns for scale, security, and maintainability, and every key decision is documented.
Engineer-ledAn engineer signs off every architecture decision.
Design System and Accessibility
Every screen is built from a shared design system in Figma, with AI-assisted accessibility checks and design-to-code handoff.
Engineer-ledDesigners own every design decision and its consistency across devices.
02Code Quality and Security
AI-Assisted Development, Human-Owned Code
Developers use Claude Code, GitHub Copilot, and Cursor for scaffolding, boilerplate, and repetitive patterns, never for unreviewed logic.
Engineer-ledEngineers own the logic and review every AI-generated change.
Coding Standards, Git Flow and Peer Review
Code follows agreed conventions and a Git Flow branching model, and AI-assisted review flags issues on every commit.
Engineer-ledA second developer reviews every change before it merges.
Secure and Compliant by Default
ISO 27001 and ISO 9001 certified processes, SOC 2-aligned controls, least-privilege access, and encryption in transit and at rest, with HIPAA-aligned safeguards where health data is involved.
Engineer-ledAn engineer owns every security review.
03Testing and Release Safety
AI-Generated Tests and Manual QA
AI generates unit tests, edge cases, and regression suites from the codebase, and automated suites run on every build.
Engineer-ledQA engineers test the real user flows by hand before every release.
Static Analysis and CI/CD Pipelines
Static code analysis scans every commit, and CI/CD pipelines build, test, and deploy across development, staging, and production.
Engineer-ledEngineers set the quality gates the pipeline enforces and fix what the scans flag.
Release Safety
Every release has staged environments, a tested rollback plan, and alpha and beta builds for internal and external users before full rollout.
Engineer-ledEngineers review release metrics before each rollout phase.
04Reliability and Ownership
Observability
Crash reporting, performance monitoring, and usage analytics are live from launch day, with alerts routed to the team that built the product.
Engineer-ledEngineers act on every alert and track trends for the life of the product.
Data-Driven Iteration
Product analytics are instrumented from launch, and new features are prioritized from user feedback and data, not guesswork. For clients without an in-house marketing team, we also run the growth strategy.
Engineer-ledEngineers turn analytics into the backlog for each release.
Documentation, Ownership and Ongoing Support
AI keeps API references and technical docs in sync with the code, and maintenance plans keep the product secure and up to date.
Engineer-ledYou get full repository access and IP ownership, and one in-house team stays accountable for the outcome.