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Modern software delivery is no longer constrained by team size.
We help organizations ship up to 2x faster by moving from traditional delivery models to AI-enabled engineering systems.
Achieve higher quality with no proportional headcount growth by embedding AI across the entire SDLC.
Manual coding throughout every phase. AI tools are absent from the official engineering workflow. Team output scales only with headcount, and delivery speed is constrained by the volume of work each engineer can absorb.
AI-powered IDE tools — Copilot, Cursor, Claude Code, Codex — suggest code line by line or block by block. Engineers review and accept each suggestion. Productivity improves at the individual level, but the delivery structure and process remain unchanged.
AI agents complete multi-step tasks — writing tests, refining requirements, generating pull requests — and hand off to engineers for review. Workflow design shifts from individual productivity to coordinated human-AI delivery. This is where Crunch-IS operates today.
AI coordinates full SDLC phases autonomously. Engineers set goals and approve outcomes. The tooling and reliability standards required to operate at this level responsibly in production are still maturing. We track its development and will integrate Level 3 capabilities as they stabilize.
AI handles cognitive overhead across the SDLC — from drafting requirements to generating tests. Delivery cycles compress without adding engineers or restructuring the team.
Compact pods of 3–5 senior specialists replace large, fragmented teams. Output now scales with AI, reducing costs without sacrificing quality.
Automated testing, AI-assisted review, and documentation reduce defects and rework. Quality gates run continuously.
New products and workstreams don’t require proportional hiring. AI-enabled pods absorb increased scope without restructuring the team or extending timelines.
We embed GenAI tooling into your existing delivery model — AI-assisted coding and reviews, automated test generation, and AI-supported documentation and requirements. No structural overhaul required. Your teams keep their workflows; we optimize them layer by layer, improving efficiency without disrupting what already works.
A compact unit of 3–5 senior specialists — spanning product, design, and engineering — where humans and AI agents operate as a single system. Minimal handoffs, maximum ownership, and end-to-end delivery within one pod. Built for teams launching new products, validating PoCs, and scaling platforms without scaling headcount.
We help you design and implement your own AI-enabled engineering model — from AI readiness assessment and tooling selection through SDLC workflow design, team enablement, and standing up your first internal AI Pod. The goal is systematic adoption, rather than perpetual dependency on an external partner.
We integrate tools such as GitHub Copilot, Cursor, Claude Code, and Codex into engineer workflows. Suggestions are reviewed and accepted by engineers, maintaining code quality while reducing authoring time across the team.
AI generates unit, integration, and regression tests from existing code and specifications. Test coverage expands without a proportional increase in manual effort, and edge cases surface earlier in the cycle — before they reach production.
AI tools structure, refine, and validate requirements against architectural constraints before development begins. Ambiguities are resolved early — before they become mid-sprint rework.
AI analyzes proposed architectures for structural risks, scalability constraints, and dependency conflicts. Engineers retain decision authority; AI compresses the evaluation cycles and surfaces tradeoffs earlier.
Automated review pipelines flag security vulnerabilities, performance regressions, and style deviations before code reaches human reviewers. Review time is spent on logic and architecture rather than formatting and syntax.
Technical docs, API references, and architecture decision records are generated and kept up to date as the codebase evolves. Documentation debt does not accumulate between releases.
We design custom AI workflows tailored to your SDLC — from prompt engineering for specific engineering tasks to multi-agent pipelines for delivery automation. Workflows are built to your stack, not generic templates.
Pods consist of 3–5 senior specialists. There is no pyramid staffing and no junior-heavy delivery with a senior layer that rarely touches the code.
One pod owns the full delivery lifecycle — from concept to production. One team, one point of accountability, and no coordination gaps between phases.
Tools and agents are configured for your stack and workflows before delivery starts.
AI workflows, tooling decisions, and architectural rationale are documented and handed over at engagement close. Your team inherits a system that it can operate and extend without us.
As an AI-driven software development company, we don’t advise on AI adoption from the outside; we deliver it from the inside.
We don’t layer AI on top of traditional team structures. We restructure delivery around compact pods where AI and engineers operate as one unit — fewer people, full ownership, less coordination overhead.
AI is not limited to coding assistance. We embed it into every phase: requirements, architecture, QA, documentation, and deployment.
Our AI-assisted Angular-to-React migration using microfrontends demonstrates end-to-end AI-enabled delivery in a live production environment — including architecture, implementation, and testing.
They come up with lots of scalable and best-practice ideas that enable us to achieve what we have right now.
Crunch-IS was great at leading us on what good should look like and bringing our ideas to life for evaluation and review.
Crunch-IS impressed me with is the quality of developers. It is simply much better than in any other company we’ve tried working with.
We are impressed with their performance, how they are engaged with product development, and how they care about the product.
It’s been a very enjoyable experience. All has been fantastic about communicating, and working.
Our partnership with Crunch-IS has been an invaluable resource as we’ve scaled.
What I found in Crunch-IS was the technical competency, ability to think outside the box, and very good English.
They have a large impact on the whole architecture we ended up with. Overall, I’ve been pleased and impressed with everything Crunch-IS did for us.
Crunch-IS impressed us with their technical excellence and the sheer ‘meeting of minds and cultures’ and open, honest exchanges. This was not a decision we took lightly, and 5 months into the engagement we continue to be really impressed with how Crunch-IS approach the collaboration, and we’re delighted with the calibre of the team we have working with us.
Working with Crunch-IS was easy. They were patient with us as we worked through the contract questions and patient again as we were getting our billing set up. The design work was elegant and the developer delivered the app we had contracted for on time and good quality.
The team at Crunch-IS was very nice to work with. They worked diligently and were very professional. When questions or technical problems arose, they answered them quickly and were always ready and kind about helping us.
Crunch-IS developer helped us deliver an important feature for our project much faster than expected.
Crunch-IS facilitated a seamless, collaborative effort throughout the project. The team maintained open lines of communication, kept detailed records of task assignments, and adhered to project timelines. Their expertise regarding both design and development sets the company apart.
Crunch-IS provides ongoing development support that meets project requirements as needed. Despite their offshore location, they are easy to work with and accessible through communication channels
The best vendor we could possibly find. Crunch-IS blends practical development with innovative design principals. Internal and external feedback to app development has been positive. They complete project milestones by the planned schedule and deliver within budget. The team’s responsive and willing to engage in productive dialogue.
Crunch-IS exceeded my expectations. I appreciated the developers’ resourcefulness in the face of constraints and complex requirements. The company able to deliver and complete the project successfully.
The project has gone smoothly so far. Crunch-IS has an exceptional work ethic. Consequentially, they’ve been able to tackle every challenge that comes their way. They manage day-to-day work on their own well but also communicate with us regularly to make sure we’re on the same page.
The app Crunch-IS worked on is clean, streamlined, and functions as intended. Crunch-IS goes above and beyond to ensure that both teams are in tune. The passionate team crafts an enjoyable experience through their positive demeanor and effective project management skills. The team was very friendly, and working with them was a pleasure.
AI-enabled software development uses AI tools to support engineers across the full development lifecycle — code generation, testing, documentation, architecture, and review. Engineers retain control of critical decisions and judgment calls. The tools accelerate work that doesn’t require senior engineering judgment, while humans own the decisions that do.
Speed and consistency are the most direct gains. Routine tasks move faster, defects surface earlier in the build cycle, and your engineering team has more capacity for the work that requires their expertise. Applied within a structured process, AI augmentation also reduces rework by catching issues before they compound across sprints.
Vibe coding generates output quickly with minimal review or structure. AI-augmented development applies the same tooling within a defined process — with quality gates, test coverage, human review, and documentation standards built in. The output meets the same production bar as hand-written code, only the speed changes.
Yes, when built correctly. MVP development through AI-assisted coding follows the same production architecture standards as a manually written build. The foundational decisions — data model, service boundaries, infrastructure — are made by engineers. That is what determines whether an MVP scales.
It reduces the hours spent on lower-complexity work, thereby lowering that portion of the build cost. Total project cost depends on the scope and the quality standards the system needs to meet, both confirmed during scoping.
AI tooling handles more of the routine work, so the team moves faster without changing what production-ready means. Review, testing, and documentation are still required — they just take less time to complete.
It is, provided the workflow includes structured review, testing, and documentation standards. AI tooling speeds up delivery; the process around it is what keeps production quality intact. When review gates, acceptance criteria, and test coverage requirements are built into the workflow from the start, AI-augmented output meets the same bar as hand-written code.
Yes. We assess the existing codebase first to understand its structure, test coverage, and risk areas. From there, we apply tooling that accelerates the work without introducing new fragility. For legacy systems, that assessment is especially important — AI tooling is most effective when the codebase has clear module boundaries and adequate test coverage to validate changes against.
Talk through your idea with an engineer on a free assessment call.
Send an outline, a spec, or three sentences about where the system has stopped. One conversation is usually enough to know whether we’re the right fit.
Custom software, AI & ML, data and analytics, cloud, and security — from early scoping to long-term delivery.
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