What we build / AI · flagship
AI apps that ship to production, not to a demo.
We don't bolt AI on at the end. LLM integration, RAG architectures, agents, and intelligent automation are designed in from day one — governed by senior architects. Not an AI wrapper. A production AI team.
Book a call →AI in production
Production AI, not a demo.
Our AI work is in production today: Kenlo LYA is an AI SDR qualifying real-estate leads across WhatsApp and Meta. VastMindz measures health vitals from a phone camera using computer vision. Cray runs behavioral AI safety inside a consumer app. Not proof-of-concepts — shipped, monitored, and maintained by the same architects who built them.
LLM integration with fallback chains
Every LLM call we write has a fallback path — cached, deterministic, or degraded-but-functional. Models have downtime and rate limits; your product shouldn't.
RAG systems and vector stores
Retrieval-augmented generation for context-aware AI — product documentation, knowledge bases, enterprise data. Built with proper chunking, embedding, and retrieval architecture.
Agentic workflows
AI that acts, not just answers — multi-step agents with tool calls, decision trees, and human-in-the-loop escalation at the right checkpoints.
Evaluation and drift monitoring
Every AI feature ships with an eval suite and post-launch drift monitoring. Quality baselines established before go-live, alerted when they degrade.
What we build
Real AI, in real products.
LLM integration & fine-tuning
Model-agnostic integration with evaluation, fallbacks, and guardrails around every call.
RAG architectures & agents
Retrieval-augmented systems and agentic workflows that act, not just answer.
Intelligent automation
AI woven through the product to accelerate every layer — not a chatbot in the corner.
Governed delivery
Every automated action reviewed under the six-gate pipeline. No research theatre.
Proof
AI we have in production now.
How we build it
AI-native delivery, governed by architects.
The same five-step delivery model behind every Wegile build. Not an AI wrapper — a production AI team.
Under the hood
Model-agnostic, production-grade.
Production AI engineering — evaluation, fallbacks, and governance around every automated action.
Questions AI app buyers ask us
Before you start. Straight answers.
We architect AI features into the product from the start — evaluation, fallbacks, governance, and monitoring are part of the design, not added at the end. The result is AI that holds up in production, not demos that break under real usage.
Yes. We add LLM integration, RAG, and agents to existing products incrementally. The six-gate governance pipeline applies to every AI addition — security scan, architect review, staging validation before production.
Three layers: an evaluation suite testing output quality against labeled examples before any merge, architect review on every AI-generated diff, and post-launch drift monitoring. When output quality degrades, we're alerted before users report it.
The same pod monitors it. Model performance, latency, cost, and quality are tracked continuously. When a model provider changes behavior or quality drops, we respond. AI in production is an operations discipline — we run it like one.
We are model-agnostic — we've worked with OpenAI, Anthropic, Gemini, and open-source models depending on the use case, cost profile, and latency requirements. We help you choose based on your product's needs, not a preferred vendor.
Ready for production AI? Not a proof-of-concept — the real thing.
Response within 24 hours. After the architecture call, a named pod and day-one plan arrive within 4 business hours.