AI Software Development Services

Whether you're a founder with a validated idea, a team with an AI-generated prototype that needs real engineering, or a company building internal platforms, InTech delivers product engineering as a nearshore partner. Not outsourcing. Not staff augmentation. Product teams that own outcomes.

When You Need AI Software Development

Founders with validated ideas who need speed. You've tested product-market fit. You have early customers or clear demand signals. You need an MVP or product extension in 30 days without the chaos of hiring a full engineering team or managing a traditional agency.

Teams with AI prototypes that need productionization. You've built something with Claude, ChatGPT, or Cursor. It works in a sandbox. Now it needs real infrastructure, security review, observability, and the engineering discipline to run at scale.

Companies building new internal platforms. Process automation, business operating systems, internal tools. These projects typically get deprioritized internally because your engineering team is staffed for product. We build them fast.

SaaS companies needing engineering capacity. Your roadmap is loaded. You need an experienced engineer or a small team to ship features, own a new product area, or accelerate development without hiring.

Organizations exploring AI-enabled workflows. You want to embed AI into existing products, build AI-assisted processes, or experiment with AI-native features. You need engineers who understand both AI systems and production-grade architecture.

How We Build: The CRAFT Methodology

Before we write code, we align on intent. The CRAFT methodology (Context, Rationale, Automate, Fortify, Telemetry) is how we ensure every feature, every product launch, and every engineering decision connects to measurable outcomes.

Every project starts with a written contract defining the outcome, scope, success metrics, and constraints. This isn't a legal document. It's shared clarity. Founders, CTOs, and engineers use it to stay aligned as the work unfolds.

AI-Assisted Development with Engineering Judgment: Our engineers use AI tools to generate scaffolding, boilerplate, and first drafts. Every AI output is reviewed. Architecture, security, performance, maintainability, testing. AI accelerates the mechanical parts. Engineers own the judgment.

Fortify Gates: Before shipping, all code passes defined gates. Tests pass. Rollback is validated. Observability is live. We don't guess whether something works. We verify.

Telemetry from Day One: After launch, we measure what actually happened. Did users adopt the feature? Did the infrastructure perform? Did the outcome match intent? This informs the next iteration.

This approach works across MVP development, feature delivery, platform building, and internal tooling. The methodology scales from a single engineer to a multi-person team.

Delivery Models: The Pod System

We structure engagements as dedicated pods. Each pod includes the core team plus proportional access to DevOps, QA, and UI/UX support.

Express Pod

Fixed-fee. 30 days. One engineer.

For founders and product leaders who need a deployed MVP or focused product iteration fast. This is Express Pod: a fixed scope, structured delivery cadence, and a working product shipped in 30 days.

What's included: Intent Contract. Four-week cadence with defined milestones. Deployed MVP or feature. Telemetry Plan. Post-MVP roadmap. You own the GitHub repo and infrastructure from day one.

Best for: Founders validating a new product direction. Teams testing an AI-enabled feature. Companies shipping a focused internal tool.

Build Pod

Predictable monthly retainer. One dedicated full-time engineer. Two-month minimum.

For product teams that need sustained engineering capacity and runway to build. A single engineer, a Product Delivery Lead (PDL) orchestrating milestones and context, and flexibility to expand scope.

What's included: Dedicated full-time engineer. PDL coordination. DevOps and QA support. Deployed product and ongoing development. You own infrastructure.

Best for: SaaS companies extending product. Startups building a complete MVP over 2-3 months. Teams needing a reliable engineering partner on their roadmap.

Scale Pod

Predictable monthly retainer (two engineers).

For complex platforms, multiple workstreams, or sustained product development at scale. Two engineers minimum. Add capacity as your scope grows.

What's included: Multiple dedicated engineers. PDL coordination and architecture oversight. Full DevOps, QA, and UI/UX support. Deployed and maintained product. You own infrastructure.

Best for: Companies building new product lines. Teams running parallel features or platforms. Organizations needing production-grade engineering support at scale.

Infrastructure: You Own It

Every project runs on client-owned infrastructure. GitHub for code. Railway for deployment. Supabase for databases. Cloudflare for edge and DNS. Our team configures, deploys, and maintains it all. But you own the keys, the repositories, and the infrastructure. No vendor lock-in.

This is non-negotiable. The code, the infrastructure, the data. You own it.

What You Receive

A deployed product. Not prototypes or designs. A shipped MVP, feature, or platform that users can access.

Ownership of code and infrastructure. Every repository is yours. Every infrastructure resource is configured under your account. You can take the code and infrastructure anywhere, any time.

Documentation. Architecture decisions. API docs. Deployment procedures. Future engineering teams should understand what was built and why.

Telemetry and success metrics. Real data on whether the shipped product met its intent. User adoption. Infrastructure performance. Conversion metrics if relevant.

Post-MVP roadmap. What we learned building the MVP. What to build next. Where the engineering challenges are.

Why InTech For AI Software Development

The DORA Report 2025 found that 80% of developers report productivity gains with AI coding tools. But the same report shows that 59% are concerned about code quality. Our approach bridges that gap. AI accelerates execution. Engineering discipline ensures quality.

A 2025 RCT by METR (arXiv) showed that experienced developers with AI access actually saw task completion time increase slightly on complex, mature projects. The insight: AI amplifies judgment. It doesn't replace it. The best outcomes come from engineers using AI as a tool, not treating AI output as a destination.

Most organizations implementing AI in software development haven't redesigned their workflows. They're running traditional development processes with AI in the middle. McKinsey found that 78% of organizations use AI in at least one function, but only 21% have redesigned workflows to get real results. We've redesigned the workflow. Intent Contract before code. AI-assisted development with engineering review. Fortify gates before shipping. Telemetry after launch.

This is product engineering for the AI era.

Frequently Asked Questions

How is this different from hiring a developer or contracting an agency?

A developer hire takes weeks to onboard, costs overhead, and commits you long-term. A traditional agency executes tickets without product context. InTech is in the middle: an experienced, dedicated team that owns outcomes, understands your product, and ships fast. You get the depth of a specialized hire without the hiring friction or employment overhead.

What if we need to change scope mid-project?

Scope changes are normal. For Build and Scale Pods, scope adjustments are part of the ongoing relationship. For Express Pod, we've locked scope, price, and timeline at the start. If priorities shift, we discuss it upfront and adjust the contract. The Intent Contract is the agreement on what we're solving for. Everything flows from that.

Who owns the code and infrastructure?

You do. Every line of code goes to your GitHub account. Every infrastructure resource is configured under your AWS, Vercel, Railway, or Supabase account. You have complete ownership and portability from day one.

How does the AI part work? Aren't you just using ChatGPT to write code?

AI assists with scaffolding, boilerplate, and first drafts. Every AI output is reviewed by an experienced engineer for security, performance, architecture, and maintainability. We're not letting AI be the judge. AI accelerates the mechanical parts. Engineers own the judgment.

What's the typical timeline from intake to deployed product?

Express Pod is 30 days. Build Pod is 2-3 months depending on scope. Scale Pod timelines depend on complexity and team size. We provide a detailed timeline in the Intent Contract before we start.

What if the product doesn't work or we need to pivot?

That's what the Telemetry Plan is for. We measure what actually happens after launch. If users aren't adopting the feature, if the infrastructure isn't performing, or if the market direction shifts, we use data to inform the next decision. You own the product. You decide what's next.