Entry point
Generative AI development, RAG system development, LLM applications
What does Solvrz do?
Solvrz Labs Ltd. is an AI product studio and software engineering partner that designs and builds AI automation, AI MVPs, document AI, generative AI systems, and fullstack product platforms.
What is RAG system development?
RAG system development designs retrieval, context preparation, prompt architecture, evaluation, and application workflows so an LLM can answer from approved business knowledge instead of relying only on its general model memory.
Best fit
You need an LLM-powered product with clear tasks, users, and evaluation rules.
You need retrieval, prompt architecture, output review, and application UX built together.
You want to reduce hallucination and quality risk before scaling usage.
Not the best fit
You only need a generic chatbot embedded without workflow design.
You cannot define what good, bad, or risky AI output looks like.
You want model access alone without product engineering or quality controls.
The Problem
Generative AI products fail when the task, data, and evaluation are not defined before the model.
Generative AI demos work in a notebook but fail when exposed to real users, varied inputs, and production data.
Teams select a model before they have defined the task, evaluation criteria, or data boundary the model needs to serve.
Outputs are inconsistent, hard to audit, and have no mechanism for improvement after the first release.
Why It Fails
Raw LLM access does not become a reliable product without system design.
Prompt chains that work on sample inputs but break on edge cases that happen every day in production.
RAG systems that retrieve the wrong context, hallucinate citations, or cannot explain why they answered as they did.
Evaluation left to subjective review — no test set, no metrics, no regression catch for model or prompt changes.
Applications shipped without safety constraints, output review, rate limits, or operator-visible quality signals.
Solvrz Approach
We build generative AI products as evaluated, governed systems — not prompt experiments.
Reliable generative AI products need task clarity, retrieval quality, prompt discipline, evaluation coverage, and safety controls. The work combines AI architecture, product engineering, and launch governance from day one.
Task definition
We start by clarifying what the generative task actually is — the input, expected output, constraints, and user context — before selecting any model or framework.
Prompt and retrieval design
We design prompt architecture, context retrieval (RAG), output parsing, and fallback handling for reliable, testable generative behaviour.
Evaluation framework
We build an evaluation suite — test cases, quality metrics, human-review samples — so every prompt, model, or data change can be measured before it ships.
Safety and governance
We implement output filtering, confidence thresholds, operator review queues, audit logging, and rate controls for production-grade AI applications.
Comparison
RAG system vs generic chatbot.
Generic chatbot
A generic chatbot can answer broad questions, but it may not retrieve the right business context, expose evidence, or fit the workflow where the answer is used.
RAG system
A RAG system connects retrieval, source grounding, prompt rules, evaluation, and review controls so teams can test answer quality and improve the knowledge layer over time.
Market Fit
Serving organisations in the US, Canada, the UK, and Europe.
US and Canada
Fit for startups, SaaS teams, and SMEs building LLM applications, internal assistants, AI search, or knowledge workflows that need a practical launch path.
UK and Europe
Fit for organisations that need responsible generative AI, source-grounded answers, evaluation coverage, and governance for sensitive business workflows.
Use Cases
Generative AI works best on language-intensive tasks with clear quality criteria.
The best first project is usually a workflow where human review already exists and where AI assistance can reduce effort or improve consistency.
Document intelligence, extraction, and summarisation workflows
Intelligent search over internal knowledge bases and product catalogues
Customer support triage, draft generation, and response workflows
AI-assisted content generation with human review and brand guardrails
Code generation, review, and developer tooling integrations
Structured data extraction from unstructured text, documents, and transcripts
Build Sprint
From generative AI idea to evaluated, production-ready application.
The first engagement should create clarity: what to generate, how to evaluate quality, what the system needs to do consistently, and what the launch requirements are.
01
Explore
Define the generative task
Clarify the input type, output format, user context, data quality, evaluation criteria, and risk constraints before model selection.
02
Design
Architect the generative system
Design prompt chains, retrieval layers, output validation, safety controls, integration endpoints, and evaluation test sets.
03
Build
Ship an evaluated AI application
Implement the model layer, retrieval pipeline, API surface, UI integration, operator interface, and observability tooling.
04
Evaluate
Measure and improve quality
Run the evaluation suite, collect operator feedback, track quality regressions, and establish an improvement cadence.
Deliverables
What a serious generative AI engagement should produce.
Generative task definition and constraint document
Prompt architecture, retrieval design, and output schema
Evaluation test suite with quality baselines
Production-ready AI application or integration
Safety controls and operator review interface
Observability, rate management, and improvement roadmap
FAQ
Common questions about generative AI development.
What does generative AI development involve?
Generative AI development covers defining the task, designing prompts and retrieval systems, building the application layer, creating an evaluation framework, and implementing the safety and governance controls needed for production usage. Useful generative AI products need more than a model selection — they need a complete system design.
What is the difference between generative AI development and AI automation?
Generative AI development focuses on tasks involving language generation, summarisation, extraction, or synthesis — where outputs are variable and evaluation requires quality metrics. AI automation is broader and may include deterministic workflows, structured data processing, and systems where rules govern most of the decision path.
How does Solvrz approach RAG system development?
Solvrz designs retrieval-augmented generation systems by starting with the retrieval quality — chunk strategy, embedding design, query expansion — before optimising prompts. A poorly designed retrieval layer cannot be fixed by better prompts. The evaluation suite includes retrieval quality metrics alongside generation quality metrics.
When is generative AI not the right approach?
Generative AI is not the right approach when the task has deterministic answers, when output quality cannot be evaluated, when latency requirements are incompatible with inference cost, or when the risk of incorrect outputs is too high without robust human review. Solvrz evaluates these constraints before recommending an architecture.
When should a team choose RAG over a generic chatbot?
A team should choose RAG when answers need to be grounded in company documents, product knowledge, policies, support history, or other approved sources. RAG is strongest when retrieval quality, citations, permissions, and evaluation are part of the product requirements.
Next Step
Bring a language task. Leave with a generative AI build path.
Solvrz can help scope whether the right move is a RAG system, prompt chain, fine-tuned model, AI-assisted workflow, or a full generative AI product.