We design, build, and deploy AI solutions for your product — chatbots, agent workflows, fine-tuned models, RAG pipelines, and model optimisation. You focus on your business. We handle the AI.
Technologies we work with
We don't sell software — we deliver tailored AI solutions. From chatbots to complex agent workflows and model tuning, we handle every layer of the stack.
We bring the expertise so you don't have to hire a full AI team. Fast delivery, clear process, real results.
We follow a lean, collaborative process that gets your AI solution live fast — with clear milestones at every stage.
End-to-end encryption, zero data retention by default, and on-prem or private-cloud deployment — architected to support GDPR and enterprise security requirements.
We integrate AI into your existing stack — Slack, Notion, Salesforce, HubSpot, custom APIs, and more.
Every engagement ships tested, documented, and deployed into your environment — with support after launch.
We work with all leading models. You choose, or we recommend based on your requirements and budget.
Every Anvexia client gets a live project dashboard. Watch your fine-tuning job run, review chatbot responses, test agent workflows, and approve before go-live — all in one place.
We built Anvexia Nexus for regulated enterprises: one knowledge-driven platform that assists developers, QA, support, and architects — 100% on-premises, so source code and production data never leave the network.
Automated merge-request reviewer — incremental diff analysis, coding standards, security, performance, architecture review, and bug detection with AI suggestions and team metrics.
Builds a semantic understanding of your whole codebase — dependency & call graphs, API relationships, class hierarchy, semantic search, impact analysis, and auto-generated docs.
Correlates tickets, commits, releases, logs, and diagnostics to surface a probable root cause with confidence score, evidence chain, similar incidents, and suggested fixes.
The reasoning core inside Beacon — evidence collection, hypothesis generation, knowledge retrieval, historical learning, confidence scoring, and explainable root-cause ranking.
A lightweight, read-only agent for customer environments — gathers logs, configuration, and service status into secure diagnostic bundles and runs predefined health checks.
A Model Context Protocol layer exposing structured knowledge and tools — repo search, API mapping, dependency lookup, impact radius, code navigation — securely to any LLM.
The central intelligence layer linking repos, APIs, classes, methods, tickets, commits, releases, infrastructure, and people — powering semantic navigation and impact analysis.
Multi-LLM orchestrator across Claude, OpenAI, Gemini, and local Ollama models — prompt orchestration, tool selection, context assembly, cost optimization, caching, and guardrails.