AI MVP build and launch
We scope the smallest product that tests your core AI value, build it end to end with authentication, billing hooks and analytics, and ship it to early users quickly.
Generative AI DevelopmentAI for Startup
AI for startups from prototype to product.
We build AI for startups whose product is the model experience itself, helping founders move from a promising demo to something customers rely on. We focus on the hard parts that decide survival: evaluation, cost per request, reliability, data rights and an architecture your future team can own.
- Fast AI MVPs
- Evals from day one
- Founder-owned IP
01 Overview
An engineering partner for AI-first founders
Demos are easy now; durable AI products are not. Founders discover that the prompt that impressed investors fails on real user inputs, inference bills grow faster than revenue, and switching model providers breaks everything. Our approach to AI for startups starts with the user problem and a small evaluation set, then builds the thinnest product that proves value, instrumented so you can see quality, latency and cost from the first customer.
We pick architecture for your stage: hosted foundation models with prompt engineering and retrieval for speed, fine-tuning or smaller open models when unit economics or data control demand it, and agents only where multi-step autonomy actually helps users. You own the code, prompts, evaluation sets and data pipelines, documented so your first in-house engineers can take over without a rewrite.
02 Challenges
What AI solves in startup.
The recurring problems we hear from startup teams — and where AI makes the biggest difference.
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CH-01
Demo works, production breaks
Prototypes built on a few happy-path prompts hallucinate, time out or behave inconsistently once real users arrive with messy inputs, edge cases and unexpected requests.
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CH-02
Inference costs eat margins
Using the largest model for every request, with long contexts and no caching, can make each customer cost more to serve than they pay, especially on usage-heavy plans.
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CH-03
Thin moats and model churn
Foundation models improve monthly, so products that only wrap an API risk being copied, and tightly coupled code makes switching providers or models painful.
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CH-04
Small teams, too many choices
Founders must choose models, vector stores, frameworks and hosting with limited engineering capacity, and early wrong choices create technical debt that slows every later release.
03 Use cases
AI use cases for startup.
Proven applications we design, build and run in production — each delivered through one of our core services.
RAG over customer or domain data
Retrieval pipelines ground answers in your customers' documents or proprietary domain data, with citations and access controls, turning unique data into a defensible product advantage.
RAG DevelopmentAgentic workflows for your product
Tool-using agents complete multi-step tasks such as research, form filling or data entry for users, with guardrails, step limits and human confirmation on consequential actions.
AI Agent DevelopmentEvaluation and prompt optimization
We build evaluation datasets and automated scoring for your core tasks, then iterate prompts and model choices so quality improves measurably and regressions are caught before release.
Prompt EngineeringFine-tuning for cost and quality
When usage grows, distilling or fine-tuning smaller models on approved data can cut latency and inference cost while keeping quality on your specific tasks.
LLM Development & Fine-TuningProduction LLMOps and monitoring
Tracing, cost dashboards, caching, model routing and fallback providers keep your product reliable and affordable as traffic grows and model vendors change their offerings.
MLOps Services04 Services
AI services for startup.
Generative AI Development
GenAI products for text, image, code and document generation.
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RAG Development
Retrieval-augmented generation grounded in your private data.
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AI Agent Development
Autonomous, tool-using AI agents and multi-agent workflows.
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Prompt Engineering
Prompt design, evaluation suites and guardrails.
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LLM Development & Fine-Tuning
Custom and fine-tuned large language models for your domain.
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MLOps Services
CI/CD, monitoring and retraining pipelines for ML in production.
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05 How we deliver
From use case to production.
Every startup AI project follows the same five stages, so you validate value early and scale with confidence.
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01
Discover Typically 1–2 weeks
AI readiness assessment, data audit and use-case discovery. We rank opportunities by business value and feasibility and agree on measurable success criteria.
Output: Roadmap · data audit · success metrics
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02
Prototype Typically 2–4 weeks
A working proof of concept on your real data, scored against an evaluation set — so you see accuracy, latency and cost before committing to a full build.
Output: PoC · evaluation report · build estimate
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03
Build Sprint-based
Production engineering: data pipelines, model training or fine-tuning, RAG retrieval, agent tooling, APIs, guardrails and the user interface your team will actually use.
Output: Production code · APIs · guardrails
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04
Deploy Cloud, on-prem or edge
Secure deployment on AWS, Azure, Google Cloud, your private infrastructure or edge devices, with CI/CD, LLMOps and MLOps pipelines from the first release.
Output: CI/CD · infrastructure as code · rollback
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05
Operate Ongoing
Monitoring, drift detection, retraining and cost optimization keep models accurate and affordable as your data, users and the AI landscape change.
Output: Monitoring · retraining · cost reports
06 Built for your sector
Designed for startup realities.
Data, regulation and the systems you already run shape every architecture decision we make.
Data rights and privacy
We check that you have rights to training and retrieval data, keep customer data out of vendor training where contracts allow, and design around GDPR and SOC 2 requirements enterprise buyers will ask about.
Model-agnostic architecture
We integrate leading model APIs and open-weight models behind a clean abstraction layer, so you can switch providers, add routing or self-host later without rewriting your product logic.
Runway and iteration speed
Startups live on limited runway, so we work in short milestones tied to user learning, cut scope aggressively, and avoid infrastructure you will not need until you have real traction.
How do you build an AI MVP for a startup?
We start by defining the core user problem and collecting a small set of real examples to evaluate against. Then we build the simplest end-to-end product, usually on a hosted model with retrieval, measure quality and cost, and iterate with early users. Scope stays tight so you learn what customers value before investing in heavier infrastructure.
Should our startup fine-tune a model or use an API?
Most startups should begin with a hosted API plus prompt engineering and retrieval, because it is fastest to iterate. Fine-tuning or smaller open models make sense later when you have quality data, clear task definitions and usage high enough that cost, latency or data control justify the extra work. We help you decide using measured evaluations.
How do we keep AI inference costs under control?
We track cost per request from the start, route simple tasks to smaller models, cache repeated results, trim context, and batch background work. Pricing plans should reflect real usage patterns. With visibility into cost per customer, you can set limits and tiers that keep margins healthy as you grow.
Who owns the code and IP you build?
You do. Under our standard engagement terms, source code, prompts, evaluation datasets and documentation are delivered to your repositories, and your data stays yours. We avoid locking you into proprietary tooling, so investors and future engineers see a clean, transferable codebase. Specific terms are always confirmed in the contract before work begins.
How do you make an AI product defensible?
Defensibility rarely comes from the model alone. It usually comes from proprietary or customer-specific data, deep workflow integration, evaluation-driven quality on a narrow problem, and a user experience built around your users' real jobs. We design architecture that compounds those advantages, such as feedback loops that improve retrieval and evaluation sets with every customer.