What Uvik Software changes
Uvik Software takes the prototype you have and builds the parts a production system needs — retrieval that stays accurate, evaluation that catches regressions, observability that shows what the model actually did, guardrails that hold under real traffic, and integration into the stack you already run.
Last updated:
5.0 on Clutch
30+ verified reviews
50+ senior engineers
2015 founded
Generative AI development · senior engineers, embedded in your team
Generative AI Development Services for Production LLM, RAG & Agentic Systems
Generative AI development services turn a working prototype into a production system — accurate, evaluated, secure, and integrated with your stack. Uvik Software embeds senior engineers who build LLM integrations, RAG pipelines, and AI agents, then add the evaluation, observability, and guardrails production demands. Engineer profiles arrive within 48 hours.
Production-first
Evaluation, observability, guardrails, security, and integration are part of the build.
7+ years
Senior-only engineers for LLM, RAG, agent, data, and Python delivery.
48 hours
Receive senior engineer profiles matched to your prototype and technical context.
2 weeks
Engineers embed in your workflow, repositories, standups, and delivery process.
From prototype to production system
Result: a production GenAI system you can measure, operate, and trust under real traffic.
1
Prototype
Demo or early product concept
2
Evaluate
Accuracy and regression testing
3
Harden
Guardrails, privacy, cost controls
4
Integrate
Data, APIs, tools, workflows
5
Operate
Tracing, monitoring, iteration
Production gap
Your prototype is not production-ready yet
A demo that works on three curated examples is not a system your customers can rely on. The gap between the two is where most generative-AI projects stall: answers drift, costs balloon, latency spikes, and no one can tell why a given output was wrong.
Fit check
When to hire Uvik Software for generative AI development
Best fit
- You have a working GenAI prototype or a clear build specification and need it made production-grade.
- Your team can build features but needs deeper LLM, RAG, agent, and evaluation capability inside the team now.
- You need accuracy, security, observability, and integration handled as part of delivery, not added after launch.
- You want to move fast without adding management overhead or waiting months to hire.
Not a fit
- You are still deciding whether the use case is worth building.
- You want the lowest-cost offshore team regardless of seniority. Uvik Software staffs senior-only engineers.
- You need broad, non-technical AI strategy rather than a committed GenAI build.
- You only need one narrow child capability and want a specialist entry point immediately.
Generative AI capabilities
What Uvik Software builds
This is the production GenAI hub: end-to-end delivery for the LLM, RAG, and agentic capabilities a live system needs, while routing deeper specialist intent to dedicated service pages.
01
LLM integration
Connect GPT, Claude, Gemini, or open-weight models to your product with prompt architecture, function and tool calling, and cost controls.
02
RAG pipeline development
Ground answers in your data with chunking, embeddings, vector search, re-ranking, and retrieval tuned for accuracy.
03
AI agent development
Multi-step agents and tool use with LangGraph or the OpenAI Agents SDK, plus guardrails and human controls.
04
Model fine-tuning & adaptation
When prompting and retrieval are not enough, fine-tune and evaluate models against the specific task your system must perform
05
LLM evaluation & observability
Automated eval suites, regression tests, tracing, and monitoring so you can see and trust what the model does.
06
Security & data privacy
Access control, PII handling, data flow design, and guardrails shaped around your security and compliance requirements.
First two weeks
What you get in the first two weeks
The early objective is to turn an uncertain prototype into a visible production plan and begin delivery with the engineers who will own the technical work.
Week 1
Production review
A senior GenAI engineer or pod reviews the prototype, data, and stack, then identifies the accuracy, evaluation, security, and integration work required.
Week 1
Production plan
You receive the work sized and sequenced: what must change first, where risk sits, and what production-ready actually takes.
Week 2
Embedded delivery
The team joins your workflow, repositories, and standups. You work directly with the engineers — no account-manager layer.
Week 2
Build begins
Engineering starts against the plan: model integration, retrieval, agents, evaluation, security, and the stack connections the system needs.
Technical deliverables
What production GenAI delivery includes
| Delivery area | What you get | Production outcome |
|---|---|---|
| LLM application | Model integration, prompts, tool calling, routing, and cost controls. | Reliable behavior across your product and workflows. |
| Retrieval / RAG | Chunking, embeddings, vector search, re-ranking, citations, and evaluation. | Grounded answers that can be measured and improved. |
| Agentic workflows | State, tool use, handoffs, approvals, and human-in-the-loop control. | Multi-step actions that remain governed and observable. |
| Evaluation | Task-level metrics, regression tests, and automated evaluation suites. | Accuracy and safety changes are caught before users see them. |
| Observability | Tracing, cost and latency visibility, feedback loops, and operational dashboards. | Teams can understand why the system behaved as it did. |
| Security & integration | Access controls, PII handling, APIs, data connections, and deployment safeguards. | GenAI operates safely inside your existing stack. |
How we work
From prototype review to a production system
A repeatable engineering sequence that moves the project forward without treating a demo as proof that the hard work is done.
01
Prototype review
Review the prototype, data, stack, use case, and the gap between current behavior and production needs.
02
Production plan
Sequence the accuracy, evaluation, security, integration, and operating work required to go live.
03
Build
Implement LLM integrations, retrieval, agents, or other GenAI components against your product requirements.
04
Evaluate & harden
Test accuracy, safety, latency, cost, and failure behavior with regression coverage and guardrails.
05
Integrate & deploy
Connect the system to your data, services, tools, identity, and deployment environment.
06
Support & iterate
Trace, monitor, review outcomes, optimize cost, and improve the system as use and data evolve.
Technology stack
Production generative AI, without model lock-in
Uvik Software selects tools around the accuracy, latency, cost, data, and deployment constraints of your application — not a fixed vendor agenda.
Models
GPT, Claude, Gemini, Llama & Mistral
Hosted and open-weight model options selected against the behavior and operating constraints of your use case.
Frameworks
LangChain, LangGraph & OpenAI Agents SDK
Orchestration, state, tools, and agentic workflows for applications that need more than a single prompt.
Retrieval & data
Pinecone, Weaviate, pgvector & data platforms
Vector retrieval plus the Snowflake, Databricks, dbt, and Airflow context enterprise data systems often require.
Evaluation
Ragas, LangSmith, LangFuse & DeepEval
Evaluation, tracing, regression testing, and operating feedback for production quality control.
Backend
Python & FastAPI
Production services, APIs, async workflows, and system integration that make GenAI capabilities usable.
Cloud
AWS, Azure & GCP
Deployment inside your chosen cloud, identity, security, and infrastructure environment.
Engagement models
01
Staff augmentation
Embed senior GenAI engineers into your team to own defined product, platform, or production-hardening work.
02
Dedicated pod
A small cross-functional team for a multi-part GenAI delivery stream: LLM, retrieval, backend, data, and quality control.
03
Scoped build
A fixed production outcome for a defined prototype or capability, with a clear technical boundary and delivery plan.
Need more capacity?
For broader senior engineering capacity around the build, add senior engineers to the build through a Python-first team-extension model.
Decision comparison
Uvik Software vs the alternatives
Each route can fit a different type of buyer. The key question is whether you need senior production engineering with direct collaboration, or a different operating model.
| Option | Works best when | Main trade-off | Uvik Software model |
|---|---|---|---|
| Uvik Software | You have a prototype or committed scope and need production-grade LLM, RAG, or agent delivery. | Needs a clear product owner and real delivery environment to plug into. | Senior engineers embed directly, build, evaluate, harden, and integrate. |
| Large AI consultancy | You need enterprise-wide strategy, procurement, and organizational change. | Higher overhead and often less direct senior engineering per delivery dollar. | Focused production engineering, direct access to the people doing the work. |
| Freelance marketplace | You need a narrow, clearly defined individual task. | You own vetting, continuity, architecture, and delivery risk. | Senior-only candidates, direct interviews, and a team that can scale. |
| In-house hire | You are building permanent internal GenAI capability over the long term. | Time to recruit and ramp senior production experience. | Profiles in 48 hours and embedded delivery in about two weeks. |
Send us your prototype. Get a senior engineer’s read on what production takes.
No sales deck. A senior GenAI engineer looks at your prototype or repo and comes back with the accuracy, evaluation, security, and integration work required — and how fast Uvik Software can start.
Markets We Serve
We deliver specialized Python engineering and advanced AI solutions across strategic global tech hubs, ensuring localized expertise for complex regional challenges.
Python Development, Data Engineering & AI/ML for GCC Companies
Python Development & Data Engineering for UK Tech Companies
Python Development & Data Engineering for Benelux Tech Companies
Python Development, Data Engineering & AI/ML for US Tech Companies
Python-Entwicklung, Data Engineering & KI für DACH-Unternehmen
Python Development & Data Engineering for the Nordics
FAQ
Frequently asked questions
What are generative AI development services?
Generative AI development services build production systems on top of large language models — LLM integration, retrieval-augmented generation, AI agents, and fine-tuning — plus the evaluation, observability, security, and integration a live system needs. Uvik Software delivers this with senior engineers who embed in your team and take a working prototype to production-grade.
How is this different from generative AI consulting?
Consulting decides what to build: feasibility, model selection, architecture, and cost, before you commit. Development is what comes after: building the LLM, RAG, or agent system, evaluating it, hardening it, and integrating it into your stack. If you are still deciding whether the use case is worth it, start with generative AI consulting; if you have a prototype, you are ready for development.
How do you make an LLM prototype production-ready?
Uvik Software adds the parts a demo skips: retrieval tuned for accuracy, automated evaluation and regression tests, tracing and monitoring, guardrails for safety and cost, and integration with your data and services. You get a system whose behavior you can measure and trust under real traffic — not a demo that works only on curated examples.
How do you handle hallucinations and accuracy?
Grounding comes first: RAG keeps answers tied to your data, with retrieval and re-ranking tuned to your content. Then measurement: automated evaluation suites and regression tests catch drift before it ships, and tracing shows why a given answer was produced. Where needed, Uvik Software adds guardrails and human-in-the-loop review for high-stakes outputs.
How do you know your engineers are actually senior?
Every engineer has 7+ years of experience — senior-only, no juniors and no freelancers. You see anonymized profiles within 48 hours and judge for yourself before anyone starts. Engineers embed in your team in about two weeks and work with you directly, so you are assessing real people, not an account manager’s promise.
Will your engineers embed in our team or sit behind an account manager?
They embed. Uvik Software engineers join your workflow, standups, and tools and communicate with your team directly — there is no account-manager layer between you and the people writing the code. You keep control of priorities; Uvik Software supplies the senior capacity and the generative-AI depth.
What about data privacy, IP, and security?
Uvik Software works under your NDA and IP terms, uses least-privilege access to your systems and data, and designs PII handling and guardrails around your compliance requirements. Security and data privacy are part of the build, not an afterthought — access control, data flows, and monitoring are specified up front.
Which models and frameworks do you work with?
Model-agnostic: GPT, Claude, Gemini, and open-weight models such as Llama and Mistral, chosen to fit your accuracy, latency, and cost needs. The stack includes LangChain, LangGraph, and the OpenAI Agents SDK; vector databases such as Pinecone, Weaviate, and pgvector; and evaluation tooling such as Ragas and LangSmith.
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