AI integration at Elvnn is the practice of embedding language and multimodal models into product surfaces where they change user behavior, not just demo well. We design the prompt, the system context, the UI, the fallback states, and the evaluation loop — and we pick the model that fits the task, not the one in the headlines. Common engagements: chat-style copilots that act on user data, semantic search over a user's own knowledge base, structured-output agents that automate internal workflows, and content-generation flows tied to approvals. Every AI feature we ship comes with a written eval set, a safety/refusal policy, and instrumentation so the team can see prompt-level token cost and latency weekly. We treat AI like any other interface problem: user goal, affordance, observable outcome.
What you get
- ·Prompt design and system context
- ·Typed structured-output schemas
- ·UI for streaming, suggestion, and review flows
- ·Eval set you can run on every model swap
- ·Monitoring for token cost, latency, and refusal rate
How we work
- 01Identify the user goal and the failure cost
- 02Prototype the smallest AI feature that proves the goal
- 03Instrument before scaling prompts
- 04Tune model choice against cost/latency/quality budget
Outcomes
Selected ai integration work
Common questions about ai integration
Which model providers do you work with?
OpenAI, Anthropic, Google, Together, Groq, and any provider with a stable API. We pick against latency, cost, and eval quality — not brand.
Can you integrate with our existing product?
Yes — most AI work is inside existing products. We integrate against your auth, your data model, and your deployment pipeline.
Bringusaiintegration.
30 minutes. No decks, no pitch — just a conversation about what you're building.