We build software with AI in the loop.
30–50% lower total cost than a traditional outsourced build. 2–3x faster on well-scoped work. Production-grade because senior engineers stay in the loop — that's what AI-assisted delivery actually requires. The methodology is the product; the code is the by-product.
Ranges drawn from McKinsey's 2025 software-development research, the MIT/GitHub Copilot RCT, Google's DORA 2024–25 reports, and our own engagement data. Outcomes vary by codebase, scope clarity, stack, and how much legacy or non-API integration is involved — see where the numbers fit best.
Production software, end to end.
Generalists by design — full-stack web, mobile, backend, AI, data, integrations, dashboards, agents, and the unglamorous infrastructure that keeps systems alive after launch. AI in the loop lets a small senior team cover the surface area that used to require a department.
Data integration, ETL & connected systems
Disparate sources unified into one model. ETL pipelines that shape raw data into a structure primed for analytics and ML. Connected systems across CRMs, ERPs, ESPs, ad platforms, billing, product, and finance — wherever APIs exist, plus adapters where they don't.
Data modeling & analytics
Purpose-built data models — typically fewer than 20 tables — designed to feed BI, ML, and operational systems alike. Real-time and historical analysis, KPI tracking, and the integrated business view that lets the rest of the AI stack work.
ML & predictive systems
Model development, training, validation, deployment. Predictive segmentation, scenario modeling, recommender systems, channel and budget allocation. The same ML discipline that powers XGAIMS — built for client-specific problems.
Custom AI applications
LLM features, RAG over your data, agentic workflows, AI-generated content. Hallucination-grounded, eval-gated, cost-disciplined — the same way we run XGAIMS.
Dashboards & analytics surfaces
Executive dashboards, real-time operational surfaces, embedded analytics inside your product. From data-warehouse + BI integrations to fully custom interactive surfaces — built for the people who need answers, not for the analyst who has to maintain the report.
Agentic bots & assistants
Domain-specific AI agents that take action: customer-support agents, internal assistants for sales, ops, and HR, autonomous workflows. Built with eval suites, hallucination grounding, action gates, and audit trails — the same discipline that protects XGAIMS in production.
Full-stack web & mobile
Next.js + React 19 on the front, Python/FastAPI or Node on the back, Postgres / Supabase / S3 underneath. Production-ready front-ends and APIs, not framework demos.
Modernization & cloud
Stranded codebases lifted onto modern stacks — AI is unusually good at reading old code, and we use that to compress migrations. AWS, Vercel, Cloudflare. CI/CD, observability, cost controls.
Monitoring & maintenance
Continuous observability, incident response, periodic model retraining, dependency upgrades, security patching. The unglamorous discipline that keeps systems alive after launch — and the gap most outsourced builds leave behind.
The four-step loop, in summary.
AI generates code fast. AI inside a senior-led, spec-driven, test-first, eval-gated loop generates production-grade code at 30–50% lower cost. The discipline is the difference.
The math is simple.
| Approach | Typical cost | Calendar time |
|---|---|---|
| Traditional outsourced build | $$$ (baseline) | 6–12+ months |
| Big-firm consultancy | $$$$ (1.5–2x baseline) | 9–18+ months |
| XG · AI-native deliveryUs | 50–70% of baseline | ½ the calendar time |
Comparison ranges are illustrative for a typical mid-market custom-software engagement. Actual quotes vary with stack, scope, and team mix.
And where they don't.
The 30–50% cost reduction and 2–3x speed claims aren't universal. We'd rather tell you that during scoping than after the contract.
Where the curve is steepest
- Greenfield builds where we own the spec from day one
- Modern stacks (Next.js, React, Python, Node, Postgres) with strong tooling and AI training data
- Vendor systems with documented APIs — Salesforce, HubSpot, Stripe, modern SaaS in general
- An empowered IT counterpart who can grant access, sign off on architecture, and unblock decisions
- Clear product-side ownership of scope and acceptance criteria
- Data that's already accessible, even if messy — we can reshape it; we can't conjure it
Where the curve flattens
- Home-grown systems with no APIs and no internal expertise on the source code
- Mainframes, COBOL, and other stacks where AI training data is thin or low-quality
- IT or security review cycles measured in months — political bottlenecks erode the speed claim more than any technical issue
- Compliance regimes (FedRAMP, certain healthcare contexts) where the gating cost dominates the build cost
- Scope that changes weekly — methodology requires a stable target to compress the timeline against
- Data behind firewalls or contracts we can't reach — extraction can take longer than the build itself
We can still ship in the harder cases — the methodology still pays off — but the acceleration is smaller and the timeline is longer. We'll quote that honestly up front.
First Build · 4 weeks · capped at $20K
One concrete deliverable shipped end-to-end under the full methodology. You get the code, the methodology in action, and a senior team who's already worked with you when you decide to scope something larger.
Anchor: a traditional "discovery + small build" engagement is typically $40–60K and 8–12 weeks. First Build is half that, scoped tightly, fixed-price.
Request an estimate.
Send us the scope. We'll come back within one business day with an estimate range, the assumptions behind it, and the team who would build it.