Services · AI SaaS MVP Development
Ship your AI-powered MVP in 8–12 weeks. TechEniac's sprint-based process takes founders from idea to launched product with real AI features from Day 1, not mock-ups with hardcoded responses.
15 production AI MVPs shipped. Healthcare. FinTech. EdTech. HR Tech. Real users. Real traction. Real compliance.
AI SaaS MVP development is the process of building the smallest viable version of an AI-powered software product that delivers enough value to attract real users and validate your business thesis.
The goal is not to build everything it's to build the one AI capability that proves your product deserves to exist, then iterate based on real user behaviour.
The difference between a successful MVP and a failed one isn't the technology. It's scope discipline. The founders who ship on time are the ones who build one feature exceptionally well. The founders who run out of budget are the ones who try to build everything in V1.
“TechEniac pushed back on our original feature list and reduced it to a half-page scope document. That discipline is why we launched on time.”
Who We Help
Three founder profiles consistently come to TechEniac. We've built for all three.
You have domain expertise and a validated AI product idea, but need a technical team to architect and ship the first version. TechEniac becomes the engineering side of your founding team.
You can code but cannot build a production-grade product alone in a competitive timeline. You understand the technology you need a team that can ship while you focus on customers, fundraising, and strategy.
The MVP from your previous team is incomplete, the codebase is broken, or the team disappeared mid-project. TechEniac evaluates whether to rescue or rebuild, then executes the path that gets you to production fastest.
Our typical MVP client is a bootstrapped or pre-seed founder with a $15,000–$50,000 development budget, a 3–6 month timeline to demonstrate traction, and a specific AI capability that differentiates their product from existing solutions.
Delivery Process
A four-phase process designed to ship working products in weeks. Every phase has defined deliverables and founder review gates.
TechEniac defines the product's core value, identifies the single feature that must work perfectly in V1, maps the AI architecture (prompt engineering, RAG, agents, or fine-tuning), and produces a sprint plan. The output is a one-page MVP Scope Document covering what V1 includes and explicitly what it excludes.
Two-week sprints with demos. The founder sees working software in days, deployed to a staging environment. TechEniac builds the AI capability first not the UI. The AI pipeline must perform before the interface is polished. Each engineer works on your project full-time, no splitting attention.
Before any AI MVP goes to production, structured testing runs against real benchmarks. Healthcare MVPs are tested against clinical gold standards. Fintech MVPs are tested against regulatory requirements. Every MVP is tested for hallucination rates, edge cases, and prompt injection resistance.
Complete production deployment CI/CD, cloud infrastructure, monitoring, and load testing at 2–3x expected peak traffic. We launch with a beta group of 10–50 users, collect structured feedback for 2–4 weeks, then open to general availability.
Book a free strategy session. We'll review your idea, scope V1, and map a realistic path to launch.
Tech Stack
We select AI technology based on the MVP's accuracy requirements, budget, and time-to-market pressure. We start with the simplest AI approach that meets the accuracy threshold and add complexity only when validated by user data.
| Layer | Technologies |
|---|---|
| LLM Selection | GPT-4o for complex reasoning, Claude Sonnet for compliance-sensitive output, Gemini for multimodal and cost efficiency, Llama for budget-optimised inference. Multi-model strategies are common TalentSync AI uses Gemini (resume parsing) and GPT-4o (screening) in the same pipeline. |
| AI Architecture | Prompt engineering as the starting point for every MVP. RAG pipelines for products that answer questions about proprietary data (MortgageLens AI: 90%+ compliance accuracy). Multi-agent systems for complex workflows (TalentSync AI: 5 LangGraph agents). Fine-tuning reserved for cases where RAG and prompting aren't enough. |
| Frontend | React with Next.js and TypeScript type safety, component reusability, SSR for SEO-critical pages. |
| Backend | Node.js with Express for application logic. Python with FastAPI for AI services. |
| Database | PostgreSQL with row-level security for multi-tenant MVPs. MongoDB for flexible document storage. Redis for caching. |
| Cloud | AWS (ECS Fargate) or GCP (Cloud Run) with serverless-first architecture to minimise infrastructure costs during the early stage. |
Proof, Not Pitch
Not hypothetical capabilities. Production MVPs serving real users.
Autonomous AI recruiting platform built from zero to production. Five LangGraph agents handle the complete pre-screening workflow: job decomposition, resume parsing, screening scoring, personalised outreach, and autonomous scheduling. Built with a 5-engineer team.
Results: 68% recruiter time saved. 4x faster shortlists. 61% reduction in candidate ghosting. 50+ companies onboarded within 3 months of launch.
AI-powered course creation platform that generates complete course structures modules, lessons, learning objectives, slides, and adaptive assessments from a topic brief or uploaded syllabus. SCORM 1.2 and 2004 export enables compatibility with any major LMS. Built with a 4-engineer team.
Results: 90% reduction in authoring time. 18 B2B customers signed within the first quarter. 4.3/5 expert panel score for pedagogical quality.
Mortgage underwriting RAG platform with hybrid retrieval (vector search plus BM25 with reciprocal rank fusion). Multi-format ingestion handles PDFs, scanned images, and video training modules. Serverless on GCP Cloud Run. Built with a 4-engineer team.
Results: 85% faster guideline retrieval (20 minutes → under 30 seconds). 90%+ compliance accuracy with grounded answers and page citations. 35% infrastructure cost reduction.
AI wealth advisory platform integrated with 350+ UK banks via Open Banking (TrueLayer). 100% FCA regulatory compliance maintained through a dedicated boundary management layer. Built with a 6-engineer team.
Results: 2,800 active users within 3 months of launch. £1,840 average annual tax optimisation per user. NPS of 72 vs. UK personal finance app average of 34.
Why TechEniac
TechEniac ships AI MVPs in weeks with real AI capabilities from Day 1 not mock-ups with hardcoded responses. Every demo runs on the actual pipeline you'll launch with.
Most agencies build the UI first and bolt AI on last. TechEniac builds the AI pipeline first because that's where MVP risk concentrates. If the AI doesn't work, the UI doesn't matter.
We actively reduce scope. We tell founders what NOT to build in V1. This discipline is why our MVPs ship on time and why one client cut their original feature list to a half-page scope document and launched on schedule.
We don't disappear after launch. We stay through product-market fit, scaling, and feature expansion. Our average client relationship exceeds 2 years.
If your previous team failed, we evaluate whether to rescue or rebuild and execute the path that gets you to production fastest. We have rescued 4 failed MVPs from other teams.
“Most agencies said yes to everything and quoted us the next day. TechEniac asked hard questions, challenged our assumptions, and told us what to cut. That's when we knew they were the right partner.”
Common questions founders ask before starting an AI SaaS MVP project.
Book a free 30-minute MVP strategy session. We'll review your idea, scope V1, challenge what doesn't belong, and map a realistic path from idea to launch in 8–12 weeks.