Applied AI engineering
Building applied AI systems for real industries.
AI products, intelligent search, RAG systems, workflow automation, generative AI and custom AI engineering — from problem definition to production.
Built by Tomyest
All work- SolvTravAI search over fragmented travel inventoryLive
- AskQuoRAG agents grounded in a company's own knowledgeLive product
- Recruitment OperationsOne operations board for cross-border recruitmentIn daily operational use
- AI Space VisualizerGenerative visual commerce for furniture retailersLive · open to the public
What we build
All capabilities- Intelligent Search & RAGAnswers grounded in your data, with the source attached
- AI Agents & AutomationWorkflows that run themselves, and stop when they should
- Generative & Visual AIImage generation constrained to what you actually sell
- AI Product EngineeringThe part that decides whether any of it reaches production
How we work
From problem definition to a running system.
- 01
Discover
Which part of the work deserves a system, and which part does not.
- 02
Design
The shape of the system, where it stops, and who steps in when.
- 03
Build
Retrieval, data paths, APIs and generation — the working parts.
- 04
Control
Budgets, kill switches, and explicit limits on what the model decides.
- 05
Deploy
Into production, with gates and a rollback the team can operate.
- 06
Improve
Measured in workflow: did the work get done, with how much intervention, at what cost.
How we build
AI that works inside real systems.
Grounded in business data
AI does not invent company truth. Answers come from what was retrieved, and they arrive with the source attached.
Human fallback on the paths that matter
Which decisions require a person is decided at design time and enforced in the workflow, not asked for in a prompt.
System truth is not model output
The application owns business state. A model reads it and proposes changes to it; it does not author it.
Measured in workflows
Success is work getting done, with fewer interventions, at a known cost — not a score on a model benchmark.