AI development services that survive production
Retrieval pipelines, LLM integration and agent systems built to be measured, not demoed. The multilingual platform we shipped cut manual processing by 87% at 99.2% extraction accuracy.
What does LLM integration actually involve?
You have a product. It works. Nothing in it is a model yet, and the question is what changes.
Mostly: the plumbing around the model, not the model. On the platform behind every figure on this page — a multilingual logistics operation, client anonymised — the work was 14 disconnected data sources in 5 languages reconciled into one retrieval layer and wired into the ERP that already existed, rather than into a system running alongside it.
LLM integration services
Integrate GPT-4, Claude, Gemini, or open-source models into your product. Chat interfaces, document Q&A, content generation, code assistants.
RAG pipeline development
Retrieval-Augmented Generation systems that let your LLM answer questions based on your private data — documents, databases, knowledge bases.
What does an AI agent need that a demo does not?
A model already answers in your product, and now something has to act on what it says.
A way to know whether it was right. 99.2% extraction accuracy on that same platform is a measurement rather than an estimate, and it exists because an evaluation harness was built to take it. That harness is the line item most often cut to hit a date, and cutting it is how a system that demos well becomes one nobody trusts in production.
AI agent development
Autonomous AI agents that reason, plan, and take actions. Tool use, multi-agent orchestration, human-in-the-loop workflows.
AI infrastructure and MLOps
Infrastructure for AI workloads — GPU provisioning, vector databases, model serving, batch processing pipelines, cost optimisation.
What if the AI product is the whole product?
There is nothing to integrate into yet. The model and the business are the same build.
Then the schedule is the risk, not the model. That platform went to production in 11 weeks and took 87% of the manual processing out of the operation it replaced — one team across the pipeline, the product and the integration, rather than three handing work between them.
AI SaaS product development
Full-product development where AI is a first-class feature — not a bolted-on afterthought. Multi-tenant and subscription-ready from the first schema.
Custom ML model development
Custom ML models for classification, prediction, NLP, and computer vision. Full pipeline from data preparation to deployment and monitoring.
AI tech stack we work with
Tools and frameworks we use in production AI projects.
LLM Providers
Frameworks
Vector Databases
ML / Training
Backend
Infrastructure
How we deliver an AI project
From first conversation to production deployment.
Discovery & Scoping
We audit your data, define the AI use case, and scope the MVP — typically within one week of signing.
Architecture Design
Model selection, pipeline design, infrastructure planning, and cost estimation before a single line of code.
Iterative Development
2-week sprints with live demos. You see working AI features early — not at the end of the project.
Evaluation & QA
LLM output evaluation, hallucination testing, latency benchmarking, and adversarial input testing.
Production Deployment
CI/CD pipeline, monitoring dashboards, cost alerts, and model versioning — ready for real traffic.
Ongoing Optimisation
Post-launch support, prompt refinement, cost reduction, and model upgrades as the ecosystem evolves.
How much does AI development cost?
The published answer, before you ask for one.
An LLM feature added to a product that already exists runs $15,000–$30,000 over 2–4 weeks. A full AI SaaS MVP — custom pipeline, frontend, backend and production infrastructure — runs $40,000–$120,000 over 8–12 weeks.
The spread inside that second band is almost never about which model you use. It is about how ready your data is and how many external systems the answer has to reach: clean, structured, permissioned data means a retrieval pipeline in weeks, while scanned documents in four languages with no metadata means the data work is the project. Managed delivery is quoted fixed-price after a discovery call, with a written scope inside 48 hours.
Yarvixo delivered our AI platform in 10 weeks — on time, on spec, and with architectural decisions I could not have made better myself. Genuinely impressive team.
Common questions
What clients typically ask before starting an AI project.
Relevant case study
A recent delivery example closely related to this service scope.
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Further reading
Longer answers on cost, architecture, and the decision teams get wrong first.