TRUSTED BY TEAMS
/// About
Sodio is a generative AI development company building LLM applications, retrieval-augmented generation systems, AI agents and model fine-tuning pipelines.
Most generative AI projects fail in production rather than in the demo, so the engineering effort goes into retrieval quality, evaluation harnesses, cost control and failure handling not into the model itself.
We work with commercial APIs and open-weight models, chosen on your data, latency and cost constraints.
/// THE PROBLEM
Why Most Generative AI Projects Stall
A generative AI demo is easy to build and hard to ship. The prototype works on ten curated documents and fails on ten thousand. Retrieval returns the wrong passage. Token costs scale past the budget. Nobody can say whether last week's prompt change made the system better or worse, because there is no evaluation harness to measure it.
The engineering that matters happens after the demo. Chunking strategy and reranking decide whether retrieval finds the right source. Caching and model routing decide whether inference cost stays viable at volume. Evaluation decides whether you can change anything safely. Access control decides whether the system can touch real customer data at all.
We build for that stage. That means slower progress in week one and a system that survives week twenty.
///WHAT WE BUILD
Generative AI Development Services
Generative AI Consulting
Use-case assessment, feasibility, build-versus-buy and a costed roadmap. We tell you where generative AI will not pay back before you spend on it.
LLM Application Development
Chat interfaces, copilots, document processing and content generation built on GPT, Claude, Gemini or open-weight models, with evaluation built in from the start.
RAG & Knowledge Systems
Retrieval-augmented generation over your own documents and databases. Chunking, embeddings, vector storage, reranking and citations so answers are traceable.
AI Agents & Workflow Automation
Agents that complete multi-step tasks against real systems, with tool access, guardrails, human approval points and audit logging.
Model Fine-Tuning & Evaluation
LoRA and parameter-efficient fine-tuning, dataset preparation, evaluation harnesses and regression testing so quality is measured rather than assumed.
Generative AI Integration
Adding generative features to an existing product. API integration, prompt management, caching, cost controls, rate limiting and fallback handling.
Use-case assessment, feasibility, build-versus-buy and a costed roadmap. We tell you where generative AI will not pay back before you spend on it.
Chat interfaces, copilots, document processing and content generation built on GPT, Claude, Gemini or open-weight models, with evaluation built in from the start.
Retrieval-augmented generation over your own documents and databases. Chunking, embeddings, vector storage, reranking and citations so answers are traceable.
Agents that complete multi-step tasks against real systems, with tool access, guardrails, human approval points and audit logging.
LoRA and parameter-efficient fine-tuning, dataset preparation, evaluation harnesses and regression testing so quality is measured rather than assumed.
Adding generative features to an existing product. API integration, prompt management, caching, cost controls, rate limiting and fallback handling.
///TECHNOLOGY STACK
Our Generative AI Tech Stack
/// USE CASES BY FUNCTION
Where Generative AI Pays Back
Customer support
Ticket triage and routing, draft replies grounded in your help centre, conversation summarisation, escalation detection
Sales & marketing
Proposal and RFP drafting, call transcript analysis, personalised outbound at scale, competitive research synthesis
Operations
Document extraction and classification, invoice and contract processing, internal knowledge search, SOP assistants
Engineering
Code review assistance, test generation, documentation from codebase, log and incident summarisation
Product
In-product copilots, search over user content, content generation features, personalised onboarding flows
Compliance & legal
Contract review and clause extraction, policy question answering with citations, regulatory change monitoring
/// HOW WE SCOPE A BUILD
How We Scope a Generative AI Build
Use-case assessment
We work through the candidate use cases and identify which have a measurable outcome, accessible data and acceptable risk. Some get ruled out here, which is cheaper than ruling them out in month three.
Free solution architecture
Model selection, retrieval design, data boundaries, evaluation approach, infrastructure and a costed delivery plan including estimated inference cost at your expected volume. Yours to keep either way.
Prototype with evaluation
A working prototype on your real data, with an evaluation harness from day one so quality is measured rather than demonstrated. Typically a few weeks.
Production build
Access control, monitoring, cost controls, caching, fallback handling and human review points where the workflow needs them. Deployed into your environment.
/// ///FAQ
Frequently Asked Questions
Most work falls into four categories: applications built on a language model such as chat interfaces and copilots, retrieval systems that answer questions over private data, agents that complete multi-step tasks against real systems, and generative features added to an existing product. The engineering sits in retrieval quality, evaluation, cost control and failure handling rather than in the model itself.
A proof of concept over a defined dataset typically runs into a few weeks of work. A production system with retrieval, evaluation, monitoring and access control is a larger engagement. Ongoing inference cost depends on model choice, token volume and caching strategy, and is often underestimated. We size both build and running cost during the free solution architecture.
Commercial APIs are usually faster to reach production and better for general reasoning. Open-weight models make sense when data cannot leave your environment, when inference volume makes per-token pricing expensive, or when you need control over model versions. Many production systems use both, routing by task. We make this decision on your constraints, not on preference.
You reduce the failure rate and measure what remains. Retrieval grounding keeps answers tied to your source documents, citations let users verify claims, and evaluation harnesses catch regressions before release. For higher-risk workflows we add human approval steps. Anyone promising elimination of hallucination is overselling.
Yes, and it is often the faster route to value than building something new. We assess your data, existing architecture and where a generative feature would genuinely change user behaviour, then integrate with proper prompt management, caching, cost controls and fallback handling so a model outage does not take down your product.
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/// GET STARTED
Start With the Architecture
Tell us the workflow you want to automate and what data sits behind it. We will prepare a free solution architecture covering model choice, retrieval design, evaluation approach and estimated running cost, so you can judge the approach before committing to a build.