Recorded client call
“I find him very nice to work with and he actually delivers a very good quality. So I am really happy with him.”
Noa van der Veen · Strategy and operations, CheckyPro
AI Engineering
AI Development
Custom AI models, LLM integration, RAG pipelines, and agentic workflows: engineered for production and evaluated against your users' actual success criteria.
Partnerships and reviews
Certified partnerships
Rated by clients and team
- Clutch
Capabilities
What you get
AI development services that go from feasibility prototype to production-hardened feature, with evals, guardrails, and cost instrumentation wired in from the start.
LLM integration + orchestration
OpenAI, Anthropic, and open-source models wired into your product with the right prompting architecture, streaming, and error handling.
Fine-tuning + custom models
Fine-tuned models on your domain data, evaluated against your success criteria, not generic benchmarks that do not reflect your users.
RAG pipelines
Retrieval-augmented generation systems with chunking strategies, embedding models, and reranking tuned to your actual document corpus.
Agentic workflows
Multi-step agent architectures with tool use, memory, and human-in-the-loop checkpoints, shipped to production, not just researched.
Vector DB + embedding infra
Pinecone, Weaviate, pgvector: whichever fits your scale and latency requirements, with the indexing and retrieval patterns to match.
AI feature integration
The AI model is the easy part. We handle the plumbing: evaluation harnesses, guardrails, streaming, observability, and the UX layer around it.
Eval + safety frameworks
Automated evaluation suites, guardrail layers, and regression testing so your AI features do not regress silently between model versions.
From prototype to production: engineered, not researched.
Delivery
How we build AI products
- Week 1–2
Discovery + feasibility
We map your use case against model capabilities, estimate accuracy ceilings, and build a quick end-to-end prototype on your actual data.
- Week 3–4
Evaluation framework
Before building production features, we build the eval harness: the tests that tell you whether the AI is actually working for your users.
- Week 5–10
Build + iterate
Model selection, fine-tuning, prompt architecture, and the integration layer. Weekly demos on real data, not synthetic benchmarks.
- Week 11–12
Hardening + launch
Guardrails, fallback behaviour, observability dashboards, cost monitoring, and a runbook handoff so your team can operate and extend it.
Why Robust Devs
180+ products shipped since 2019
We do not research AI; we ship it. Every engagement includes evaluation harnesses, cost instrumentation, guardrails, and operational runbooks. Your AI features work on launch day and keep working through model version changes.
Schedule a meetingFit
Who AI development is for
Best for
- Products where AI is the core differentiator, not a bolt-on chatbot
- Teams with real user data ready for fine-tuning or RAG
- LLM features that need to work reliably at production scale
- Evaluating AI model trade-offs without the hype
Not for
- A simple ChatGPT wrapper with no custom logic: a no-code tool may be cheaper
- Pure research or paper implementation without a product ship date
- A team that wants prompts written without any engineering around them
Testimonials
What founders say after the build
Verbatim quotes from teams we shipped to production. We publish testimonials with written sign-off on file.
4.6/5across 58 verified client reviews.Read them all at source
The real work
AI engineering is 20% model, 80% plumbing
The model is the easiest part. What makes AI features reliable is the evaluation harness, the streaming infrastructure, the guardrail layer, the cost monitoring, and the UX that handles latency and fallback gracefully. We build all of it: not just the prompt.
Schedule a meetingFrequently asked questions
Straight answers about how we approach AI development: no buzzwords, no hand-waving.
OpenAI (GPT-4o, o-series), Anthropic (Claude), and open-source models via Together, Fireworks, or self-hosted. We pick the model that fits your latency, cost, accuracy, and compliance constraints, not the one trending on Twitter.
Ready to build AI that actually works?
Talk to the engineers who will build it, not a sales team. We will tell you honestly what is feasible, what the accuracy ceiling looks like, and how long it will take.