We build AI into products — not slide decks about AI.
Agentic AI applications, intelligent backends, and MCP servers that connect AI assistants to the tools and systems your business already runs.
Every company wants AI. Most of them are stuck.
The problemThey bought an off-the-shelf chatbot, bolted it onto their site, and now it hallucinates answers about products they don't sell. Or they hired a consulting firm that delivered a "strategy deck" full of acronyms and a roadmap that ends at "implementation TBD."
Or they know exactly what they want to build but can't find a team that understands both the AI layer and the production engineering required to ship it.
Pronova builds AI that actually ships. Products real users interact with. Backends that real systems depend on. Integrations that work on Monday morning, not just in the demo.
4.4x
Better conversion from AI-referred traffic vs organic search
97M+
Monthly MCP SDK downloads in early 2026
50%
Of integration vendors projected to adopt MCP by end of 2026
AI that ships, performs, and scales
What we buildAI-native products
User-facing applications with AI built into the core experience — not sprinkled on top. Intelligent search, automated workflows, dynamic content generation, natural language interfaces, and recommendation engines that actually recommend things worth clicking.
Intelligent backends and middleware
AI services that sit behind your existing applications. Your mobile app, web platform, or internal tools call our API, and intelligence comes back. RAG pipelines, embedding and vector search systems, multi-model orchestration, and custom fine-tuning workflows.
MCP server development
MCP — Model Context Protocol — is the open standard that lets AI assistants connect to external tools and data. We build custom MCP servers that give AI assistants secure, structured access to your business systems — your CRM, your database, your internal tools, your APIs.
SOP-embedded AI tooling
We don't just connect AI to your tools — we encode your standard operating procedures directly into the integration layer. Your AI assistant doesn't just have access; it knows how your team uses it. Which fields to fill in. What the approval workflow looks like. What to never do.
Use cases we've seen work
- Internal knowledge systems that answer using your actual docs and SOPs
- Customer-facing AI interfaces that reduce support volume
- Sales intelligence — AI that pulls CRM data, enriches leads, drafts outreach
- Operational automation where AI handles data gathering, humans handle judgment
- Tool-connected AI assistants via MCP for Jira, Salesforce, HubSpot, Slack
Why this matters now
For decision-makersThe market has shifted. AI isn't a competitive advantage anymore — it's table stakes. The advantage is in how well it's integrated.
Companies bolting generic AI onto existing products are creating friction, not value. The ones pulling ahead are building AI into their operational core — where it touches revenue, operations, and customer experience simultaneously.
The infrastructure is mature enough to build on. The question isn't whether to build — it's whether to build it right the first time or pay to rebuild it in 18 months.
From problem to production
How we workDiscovery — what is the actual business problem?
Not "we want AI." What's the workflow that's too slow? What's the data your team can't access fast enough? What's the decision that currently takes three people and two days? We start there.
Architecture and scoping
We map the system: what models, what integrations, what data flows, what security boundaries. You get a technical architecture document and a fixed-scope proposal before we write a line of code.
Proof of concept
For complex builds, we ship a working proof of concept in 2 to 3 weeks. Not a mockup. A functional system your team can test against real workflows.
Production build
Full engineering: frontend (if applicable), backend services, MCP servers, model integrations, testing, and deployment. We ship iteratively — working software every two weeks.
Handoff and support
Documentation, team training, and optional ongoing support. You own everything we build. No vendor lock-in, no proprietary frameworks, no "call us if you want to change anything."
Under the hood
For technical leadersModels and orchestration
We're model-agnostic. Claude, GPT-4o, Gemini, Llama, Mistral, and domain-specific models. We build orchestration layers that can swap models without rewriting your application.
MCP architecture
Resources, tools, and prompts exposed via standard transports (stdio, SSE, streamable HTTP). Authentication via OAuth 2.0. Role-based access control. Audit logging for every tool invocation.
RAG and retrieval
Vector databases (Pinecone, Weaviate, pgvector), chunking strategies tuned to your content type, hybrid search combining semantic and keyword retrieval, and reranking pipelines.
Security and governance
SOC 2 considerations, data residency controls, PII redaction in prompts, rate limiting, and permission boundaries so AI can only access what each user role is authorized to touch.
AI Development near you
Serving South TexasWe serve businesses across the Rio Grande Valley and South Texas. Click your city to see local pricing, case studies, and area-specific details.
You've got the systems. You've got the data. You're missing the layer that makes them intelligent.
Tell us what you're building — or what you wish your current tools could do. We'll tell you what's possible and what it takes.
