Service
Support agents that resolve 70% of tickets. Document processors that turn invoices into structured data in 90 seconds instead of 15 minutes. Intelligent search across scattered internal docs. We build AI features on Claude, OpenAI, and LangChain, with proper guardrails, cost controls, and evaluation baked in. No science projects.
How it runs
We work out which parts of your product or workflow would genuinely benefit from AI, and which wouldn't. Not every problem needs a language model.
A focused prototype that tests accuracy, latency, and cost against your actual data. You see real numbers before committing to a full build.
We fit the AI features into your existing product with guardrails around them: rate limiting, cost controls, fallback logic, and monitoring dashboards.
Ongoing prompt refinement, model evaluation, and cost work. We track accuracy over time and keep improving what the system returns.
Related work
AI Integration
Claude-powered support handling 70% of tickets without humans
70% tickets auto-resolved
AI Automation
AI extraction from invoices and contracts, with a 12-person team reassigned to analysis work
90% processing time cut
AI Platform
Natural-language search across Confluence, Drive, and Notion
85% search time cut
Every project is scoped and priced on its own. Tell us what you're building and we come back within 24 hours with an honest read on scope, timeline, and cost.
Current Claude models from Anthropic, OpenAI's GPT models, and open-source models through LangChain. We also work with embedding models, vector databases like Pinecone and Weaviate, and multimodal inputs. Which one we recommend comes down to accuracy, cost, and your data privacy needs.
Yes, most of our AI work is exactly that. Chat assistants, document processing, better search, content suggestions: we add the capability without disturbing what already works.
Cost monitoring and controls go into every integration: token usage tracking, caching so the same call isn't paid for twice, routing simple tasks to cheaper models, and alerts when usage spikes. The bill shouldn't surprise you.
We can deploy with API providers that don't train on your data, run local models for sensitive information, and put data masking in the pipeline. The architecture gets designed around your compliance requirements.
AI/RAG
Chatbot vs AI agent explained for founders: how each works, what each costs you, and which to build first. Talk to Evolvera about yours.
11 min read
Mobile
The real reasons apps get rejected from Apple and Google, straight from their guidelines and Apple's 2024 transparency data. Talk to Evolvera.
10 min read
Web/SaaS
Multi-tenant architecture explained: silo vs pool vs bridge, database isolation options, and how to choose one for your SaaS. Talk to Evolvera.
11 min read
Tell us what problem you want AI to solve. We'll tell you whether it's feasible, what approach fits, and what it would look like.
