[ SERVICES ]

AI Integration

We integrate AI models into your existing product or build AI-powered features from scratch. LLM pipelines, intelligent automation, and seamless embedding.

AI That Actually Ships

Most AI projects stall in experimentation. At Soleno, we focus on AI integration that makes it to production — real features your users interact with, not demos that live in a notebook. We work with founders who want to add intelligence to their products without building an ML team.

Whether you need a customer-facing chatbot, automated content generation, intelligent document processing, or predictive features, we build it end-to-end and integrate it into your existing stack.

What We Build

  • LLM-Powered Features — Chatbots, AI assistants, content generation, text summarization, and natural language search using GPT-4, Claude, Gemini, and open-source models.
  • RAG Systems — Retrieval-augmented generation that connects AI to your business data. Knowledge bases, document Q&A, and intelligent search over your content.
  • Intelligent Automation — Automated workflows that use AI for classification, extraction, routing, and decision-making. Email triage, lead scoring, content moderation.
  • AI-Powered APIs — Custom API endpoints that wrap AI capabilities for your frontend, mobile app, or third-party integrations.
  • Multi-Model Pipelines — Architectures that use different AI models for different tasks — combining speed, accuracy, and cost optimization.
  • Voice & Vision — Speech-to-text, text-to-speech, image analysis, and OCR integration for products that need multi-modal AI capabilities.

Our Approach to AI Integration

We don't start with the model — we start with the problem. What does your user need? What data do you have? What's the acceptable latency and cost per request? These questions determine the architecture, not the hype cycle.

Our typical integration process:

  1. Assessment — We evaluate your product, data, and use case to determine the right AI approach. Sometimes the answer is a simple API call; sometimes it's a custom pipeline.
  2. Prototype — We build a working proof-of-concept that demonstrates the AI feature with real data. You test it, we iterate.
  3. Production Build — We build the production system with proper error handling, rate limiting, caching, fallbacks, and monitoring.
  4. Integration — We connect the AI system to your existing product — database, API, frontend, and authentication.
  5. Monitoring & Optimization — We set up logging, cost tracking, and quality metrics. We optimize prompts, model selection, and caching to reduce costs and improve quality over time.

Models We Work With

We're model-agnostic. We work with OpenAI (GPT-4, GPT-4o), Anthropic (Claude), Google (Gemini), and open-source models (Llama, Mistral) hosted on your infrastructure. We choose based on your requirements — quality, speed, cost, and data privacy constraints.

For sensitive data, we can deploy models on your own infrastructure or use API providers with enterprise data agreements. Your data stays yours.

Cost Management

AI API costs can spiral if you're not careful. We design systems with intelligent caching, prompt optimization, model routing (use cheaper models for simple tasks), and batching to keep costs predictable. We set up dashboards so you can see exactly what you're spending and why.

Case Study: Dr. May

We built Dr. May, integrating AI capabilities into a healthcare product. The project involved custom AI pipelines, multi-model architectures, and production-grade reliability requirements.

Start Integrating AI

Every project starts with a free consultation where we assess your use case and determine the right approach. We'll tell you honestly if AI is the right solution — and if it is, we'll scope it clearly with a fixed timeline and budget.

[ FAQ ]

AI integration at Soleno — in five questions.

  • Which AI models / providers do you work with?

    We work with OpenAI (GPT-4 / 4o / o-series), Anthropic Claude, Google Gemini, and open-source models (Llama, Mistral, Qwen) via providers like Together, Fireworks, or self-hosted on Modal / Replicate. We pick the model on day one based on your latency, cost, and quality requirements — and we design the pipeline so swapping providers is a config change, not a rewrite.

  • Is AI the right solution for our problem?

    Sometimes the honest answer is no. AI fits well for fuzzy / language-heavy tasks: search, summarization, drafting, classification, conversational interfaces, code assistance. For deterministic workflows you usually want plain code, not an LLM. In the first call we'll tell you straight up which parts of your problem are AI-shaped and which aren't.

  • How do you stop hallucinations and stay accurate?

    We ground every production AI feature with retrieval (RAG) over your own data, constrain outputs with JSON schemas and function-calling, and add evaluation harnesses that score each release. For high-stakes domains we add human-in-the-loop review and confidence thresholds so low-confidence answers get escalated instead of shipped.

  • What about cost — how do you control LLM spend?

    Caching, model tiering (cheap models for routing / draft work, expensive models only when needed), streaming, prompt compression, and aggressive context trimming. Every production pipeline we ship has dashboards for cost-per-request and per-tenant budgets so you know exactly what your AI features cost before they scale.

  • Do we own the AI pipeline or are we locked into your tools?

    You own the code, prompts, evaluations, and infrastructure end-to-end. We deploy to your cloud (Vercel, AWS, GCP), use providers under your accounts, and document the prompt / eval setup so your team or another vendor can take it over later. No proprietary Soleno SaaS layer.

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