AI/ML Engineering That Ships to Production
Not just prototypes. We build AI features your users actually adopt — copilots, automation workflows, RAG systems, and internal AI tools.
From fine-tuning LLMs to deploying production-grade ML pipelines, our AI engineers integrate intelligent capabilities into your product — not as experiments, but as real features that drive adoption and ROI.
Book a CallWhy AI projects stall
Your POC worked. Production didn't.
You built a demo in a week. Six months later, it still isn't in production. The gap between a notebook prototype and a reliable, scalable AI feature is where most teams get stuck — hallucinations, latency, cost overruns, and no clear path to deployment.
Your team doesn't have ML infrastructure experience
Your developers are smart, but they've never built a vector database at scale, fine-tuned a model for your domain, or set up guardrails for production LLM responses. Hiring full-time is slow and expensive for a capability you might need intermittently.
AI vendors sell black boxes, not solutions
You're getting pitched "AI transformation" by agencies that wrap ChatGPT in a UI and call it custom AI. You need engineers who understand embeddings, retrieval strategies, prompt engineering tradeoffs, and when NOT to use AI.
You're falling behind competitors who already ship AI
Your competitors have AI-powered features in production while your team is still evaluating tools. Every quarter without AI capabilities is market share you're not getting back.
What we build
We don't sell "AI strategy decks." We write code, deploy models, and measure outcomes.
LLM Integration & Fine-Tuning
Custom LLM applications using OpenAI, Claude, Llama, or Mistral. Domain-specific fine-tuning, prompt engineering, and response quality optimization for your use case.
RAG Systems & Knowledge Bases
Retrieval-augmented generation pipelines that ground AI responses in your actual data. Vector databases, embedding strategies, and intelligent chunking for accurate, hallucination-free answers.
AI Copilots & Automation
Internal tools and customer-facing copilots that actually reduce workload. From AI-assisted code review to automated document processing and intelligent workflow automation using n8n, LangChain, or custom orchestration.
ML Pipelines & Infrastructure
Production-grade ML infrastructure: model training pipelines, feature stores, experiment tracking, A/B testing frameworks, and automated retraining. Built to scale, not to demo.
Computer Vision & NLP
Image recognition, document parsing, OCR, sentiment analysis, and text classification systems deployed as reliable microservices with monitoring and fallback strategies.
AI Strategy & Architecture
Technical assessment of where AI adds real value in your product. Architecture design for AI features including cost modeling, latency requirements, and build-vs-buy analysis.
Our AI/ML stack
LLMs & APIs
Frameworks
Vector DBs
ML Ops
Orchestration
Languages
How we deliver AI features
Technical Discovery
We audit your data, infrastructure, and product requirements. We identify where AI adds measurable value vs. where it's hype. You get a concrete plan, not a strategy deck.
Rapid Prototype
Working proof of concept in 2–4 weeks using your actual data. We validate feasibility, measure accuracy, and estimate production costs before committing to a full build.
Production Build
We build the real thing: proper error handling, guardrails, monitoring, fallback strategies, cost optimization, and user feedback loops. Not a demo — a product feature.
Iterate & Scale
Continuous improvement based on real user data. Model retraining, prompt optimization, latency reduction, and feature expansion as adoption grows.
Why Pletava
Production mindset, not research mindset
We've shipped AI features used by millions. We optimize for reliability, cost, and user adoption — not just accuracy metrics on a test set.
We know when NOT to use AI
Not everything needs a neural network. We'll tell you when a rule engine or simple heuristic outperforms an LLM. That honesty saves you months and hundreds of thousands.
Full-stack AI engineers, not just data scientists
Our engineers build the API, the infrastructure, the frontend integration, AND the model pipeline. No handoff gaps between "the AI team" and "the product team."
Frequently Asked Questions
Can't find what you're looking for? Book a call and we'll answer everything.
Book a CallHow quickly can AI engineers join our team?
Our in-house senior AI engineers can start within 1–2 weeks. For specialized roles, we source from our network of 175k+ engineers in under 2 weeks.
Do you work with our existing models or only build new ones?
Both. We integrate with commercial APIs (OpenAI, Claude, etc.), fine-tune open-source models, or help optimize your existing ML infrastructure — whatever makes sense for your use case and budget.
What's the typical cost of an AI/ML engagement?
It depends on scope. A focused RAG implementation might take 4–6 weeks with 1–2 engineers. A full ML infrastructure overhaul could be a 6-month engagement. We scope precisely during discovery so you know exactly what you're investing in.
How do you handle data security for AI projects?
We work within your infrastructure whenever possible. When we use cloud APIs, we ensure data doesn't leave approved regions, implement PII redaction pipelines, and follow your compliance requirements (SOC 2, GDPR, HIPAA).
Ready to ship AI features that users actually adopt?
Talk to our AI engineering team. No fluff, just technical depth.
Thrilled to meet you!
Let's talk possibilities