AI Development Services
We build AI that does real work, not slideware. The measure of a good AI system is whether your team trusts it on a Tuesday afternoon, and that trust is engineered.
Most AI projects stall in the gap between a promising prototype and something a business can actually run. A demo that works in a controlled screen-share is not the same as a system that handles messy inputs, unhappy paths, cost ceilings, and the occasional confidently wrong answer. Our AI development work is aimed squarely at closing that gap: taking a use case that matters, proving it can pay for itself, and shipping it with the evaluation harnesses, guardrails, and monitoring that keep it dependable once the launch excitement fades.
We work with founders and product teams who want AI woven into the product or the operation, not bolted on as a novelty. That usually starts with a hard question: where does AI genuinely reduce cost, risk, or time, and where is a plain deterministic system the better answer? We are happy to talk you out of the expensive version. When AI is the right tool, we build it end to end: retrieval that grounds answers in your own data, agents that call real tools under supervision, and integrations into the model providers with the fallbacks and cost controls that production requires.
Underneath the model calls, the unglamorous parts are what make it hold up. We invest in evaluations so you can measure quality instead of vibing it, in observability so you can see what the system actually did, and in a human-in-the-loop wherever a wrong answer would be costly. The same senior engineers who design the system operate it through its first weeks in production, so the handover is real rather than a document nobody reads.
What we build
AI Product Development & Consulting
End-to-end AI product work, from validating the use case to shipping a system your team can actually operate.
AI Strategy & Roadmapping
A prioritized, budget-aware plan for where AI earns its keep in your business, and where it does not.
Agentic AI Systems
Autonomous agents that plan, call tools, and complete multi-step work with the oversight production demands.
LLM & Generative AI Applications
Customer- and team-facing apps built on large language models, designed around real tasks rather than demos.
MCP Server Development
Custom Model Context Protocol servers that expose your data and tools to Claude and other agents safely.
LLM Integration & API Development
Resilient integration of model providers into your product, with fallbacks, cost controls, and evals.
RAG & Vector Search
Retrieval pipelines that ground model answers in your own content so responses stay accurate and current.
AI Workflow Automation
Automations that take repetitive, judgment-light work off your team while keeping a human in the loop where it matters.
AI-Powered QA & Test Automation
Test suites and QA workflows that use AI to widen coverage and catch regressions earlier.
Legacy Code Modernization with AI
AI-assisted refactoring and documentation that makes old codebases understandable and safe to change.
AI Adoption for Engineering Orgs
Tooling, guardrails, and playbooks that help your engineers ship faster with AI without losing quality.
Sales AI Enablement
AI that drafts, researches, and qualifies so your revenue team spends more time with the right prospects.
Computer Vision Solutions
Models that read images and video for inspection, detection, and classification tasks in production settings.
MLOps & Model Deployment
The pipelines, monitoring, and infrastructure that keep models serving reliably after the notebook is closed.
Data Modernization & Analytics
Cleaner data foundations and analytics that make your organization ready to use AI on its own information.
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Frequently asked questions
What does an AI development engagement with pylondev typically cost?
Most AI builds are scoped per project, depending on how much data plumbing, evaluation, and integration the use case needs. We scope a fixed first phase so you can see value before committing to a larger budget. If a cheaper deterministic system would do the job, we will tell you before you spend on AI.
How long does it take to ship an AI feature into production?
A focused AI feature reaches production on a schedule we set once we understand the use case, including the evaluation harnesses and guardrails that keep it dependable. The timeline depends more on your data readiness and integration surface than on the model itself. We sequence the work so the riskiest assumption is tested first.
Do you work with US-based startups and companies?
Yes. pylondev works with US founders and product teams, and we keep overlapping working hours for standups, reviews, and quick decisions. We are used to US contracting, security, and procurement expectations.
What AI stack and models do you build on?
We are model-agnostic and build on the major providers, including OpenAI, Anthropic Claude, and open models where they fit, wired together with fallbacks and cost controls. Retrieval runs on standard vector stores, and the surrounding application is React, TypeScript, and Postgres. We choose the model per task rather than betting the whole system on one vendor.
How do you keep an AI system from producing wrong or unsafe answers?
We build evaluation suites that score answer quality against real cases, add guardrails and permission limits around any tool the system can call, and keep a human in the loop wherever a mistake would be costly. Observability shows exactly what the system did on every run, so a wrong answer is diagnosable rather than mysterious.
Can you improve or rescue an AI prototype we already started?
Often, yes. Many teams have a promising demo that breaks on messy inputs, cost, or unhappy paths, and the work is hardening it rather than starting over. We audit what you have, tell you honestly what is salvageable, and scope the path to something you can run in production.
Tell us what needs to work better.
Describe the outcome you're after. We'll scope the build and point you at the right first step.