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AI Integration & Custom AI Solutions Services

Service Overview:

Most AI projects don’t fail because the model is weak. Research from RAND puts the enterprise AI failure rate above 80%, roughly twice the failure rate of regular IT projects, and the recurring cause isn’t the technology. It’s unclear goals, weak data, and AI bolted onto a workflow it was never actually built to fit.

We treat AI as one more part of the product, not a separate experiment running next to it. That means figuring out what problem it’s actually solving before picking a model, wiring it into the systems you already use, and building the fallback logic for when a call fails or an API times out, because it will.

That covers chatbots and support tools trained on your actual data, workflow automation that removes manual work instead of adding another dashboard, and custom integrations with providers like OpenAI, Anthropic, or Replicate wired directly into your product.

This fits three kinds of clients. A business that keeps seeing “AI” pitched to them and wants to know what would actually help. A team that has an AI feature live but it’s unreliable, slow, or nobody trusts it. Founders building a product where AI is the core feature, not a bolt-on.

Real Scenarios

When You Need This Service

You Know AI Could Help, But Not How

Everyone's talking about it. You don't have a clear use case yet, just a sense you're falling behind.

Your AI Feature Is Live But Unreliable

It works in the demo. In production, it hallucinates, times out, or gives inconsistent answers.

You're Doing Manual Work AI Could Handle

Someone on your team is tagging, sorting, or writing the same kind of thing every day, by hand.

You Need AI Wired Into Your Actual Product

Not a chatbot bolted onto the corner of a page. AI that's part of the core workflow.

Our Process

How This Service Works

Most AI failures trace back to skipping the boring part, defining what success actually looks like before touching a model. We don't skip it.

Talk Through My Project
  • 01

    Use Case & Feasibility

    We figure out what AI should actually do here, and whether it's the right tool at all.

  • 02

    Data & Architecture

    We map what data the model needs, where it comes from, and how it connects to your existing systems.

  • 03

    Build & Integration

    The AI feature gets built and wired into your product, with fallback handling for when a call fails.

  • 04

    Testing & Monitoring

    We test against real inputs, not just clean examples, then set up monitoring so failures get caught early.

Sub-Services

What's Included in This Service

AI Feature Development

AI features built directly into your product, not a separate tool bolted on the side.

Custom Chatbots & Assistants

Trained on your actual data and support content, not generic answers.

Workflow Automation

Repetitive manual work handled automatically, from data entry to content tagging.

LLM Integration

Direct integration with providers like OpenAI, Anthropic, and Replicate, wired into your existing stack.

Prompt Engineering & Fine-Tuning

Models tuned and prompted to give consistent, reliable output for your specific use case.

AI Image & Content Generation

Custom pipelines for generating images, copy, or media as part of your product's core flow.

Existing AI Feature Fixes

An AI feature that's live but unreliable, debugged and stabilized without a full rebuild.

The Difference

Why Clients Choose Sollva

Use Case Before Model

We define what problem AI is solving before picking a provider or writing a prompt.

Built for Failure Cases

Retry logic, fallbacks, and error handling included, not just the happy path that works in a demo.

Real Integration

AI wired into your actual product and data, not a standalone tool that lives on its own.

Fixed Scope

You know the cost and the timeline before work starts.

Support After Launch

Models drift, APIs change. We stay on to keep the feature working as conditions shift.

Common Questions

Frequently Asked Questions

That's the first thing we figure out together. If a simpler solution solves it better, we'll tell you that instead of selling you an AI feature you don't need.

We start by finding out why it's unreliable, usually bad prompting, missing fallback logic, or data that doesn't match the use case, then fix it.

OpenAI, Anthropic, Replicate, and others depending on the use case. We pick the provider that fits the task, not the one that's trending.

Most projects run 4 to 10 weeks depending on scope and how much of your existing system it needs to connect to.

We build with fallback handling and monitoring in place, and stay on after launch to adjust when providers make changes.

Both. Most AI failures come from the data, not the model, so we handle the pipeline end to end.