What Uvik Software changes
Uvik Software takes a validated GenAI use case or prototype and builds the production layer around it: reliable retrieval, evaluation suites, observability, guardrails, cost controls, backend services, API integrations, deployment, and operating processes.
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50+ senior engineers
2015 founded
Generative AI development services
Generative AI Development
Generative AI development services turn validated AI use cases, prototypes, and technical specifications into production systems. Uvik Software builds LLM applications, RAG pipelines, AI agents, model integrations, evaluation suites, observability, guardrails, backend services, and deployment workflows so your team can operate GenAI features under real traffic.
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.
prototype
From prototype to production system
Result: a production GenAI system you can measure, operate, and trust under real traffic.
1
Prototype
Review the demo, repo, workflow, and current model behavior.
2
Evaluate
Define metrics, datasets, regression checks, latency, cost, and failure criteria.
3
Harden
Add guardrails, access controls, human approval, cost controls, and security boundaries.
4
Integrate
Connect APIs, databases, tools, documents, CRM, ERP, support stack, and identity.
5
Operate
Add tracing, monitoring, feedback loops, cost visibility, and iteration workflows.
Production gap
Your prototype is not production-ready yet
A demo that works on a few curated examples is not a system customers or internal teams can rely on. Production generative AI systems need measurable accuracy, regression testing, retrieval quality, traceability, access control, security, latency management, cost controls, human oversight, and integration into the product or workflow they support.
Boundary
Generative AI Development vs Generative AI Consulting
Generative AI development starts when the use case is already validated enough to build. It focuses on implementation: LLM applications, RAG systems, AI agents, integrations, evaluation, observability, guardrails, deployment, and iteration. Generative AI consulting is the earlier stage: deciding whether to build, which model or architecture to choose, what risks to control, what the system may cost, and what proof of concept should be scoped.
If your question is “Should we build this?”, start with generative AI consulting. If your question is “Who can build, harden, integrate, evaluate, and deploy it?”, start with generative AI development.
| Need | Choose generative AI consulting when... | Choose generative AI development when... |
|---|---|---|
| Main problem | You need feasibility, readiness, model selection, governance, cost modelling, or POC scope. | You need implementation, RAG pipelines, LLM apps, AI agents, integrations, evaluation, deployment, or hardening. |
| Typical output | Readiness assessment, feasibility verdict, model recommendation, risk map, cost model, POC plan. | Production AI application, RAG system, agent workflow, evaluation layer, monitoring, integrations, deployment. |
| Buyer question | Is this use case worth building and what is the safest path? | Who can build and operate this system under real traffic? |
| Best first step | AI readiness or feasibility assessment. | Prototype review or technical discovery. |
Fit check
When to hire Uvik Software for generative AI development
Best fit
- You have a working GenAI prototype and need to make it production-ready.
- You have a validated use case and need senior engineers to build the system.
- Your team needs LLM, RAG, AI agent, evaluation, observability, or GenAI integration capability now.
- You need a chatbot, document intelligence workflow, internal copilot, AI agent, or LLM-powered feature integrated into your stack.
- You need measurable accuracy, regression testing, guardrails, and monitoring before launch.
- You want engineers embedded into your team rather than a black-box AI vendor.
- You need production code, not an AI strategy deck.
Not a fit
- You are still deciding whether the use case is worth building.
- You do not have a business owner, product owner, or technical stakeholder for the GenAI system.
- You need a broad non-technical AI strategy engagement.
- You want the lowest-cost offshore team regardless of seniority.
- You only need a one-off prompt written without integration, evaluation, or production requirements.
- You need AI policy, organizational change management, or company-wide training rather than engineering delivery.
What we build
Generative AI Systems Uvik Software Builds
Uvik Software builds the production components that live GenAI systems need: LLM integration, retrieval, agents, evaluation, observability, security, backend services, cloud deployment, and workflow integration.
01
LLM applications
Build product features, internal tools, copilots, document workflows, automation layers, and knowledge assistants on top of GPT, Claude, Gemini, Llama, Mistral, or other model options.
02
RAG systems
Build retrieval-augmented generation systems with chunking, embeddings, vector search, hybrid search, reranking, citations, access control, and retrieval evaluation.
03
AI agents
Build governed AI agents that use tools, APIs, state, workflows, permissions, human approval, and auditability to complete multi-step tasks.
04
AI chatbots
Build customer support, employee support, sales, onboarding, or knowledge-base chatbots that retrieve trusted information and escalate to humans when required.
05
LLM evaluation and observability
Build evaluation suites, regression tests, tracing, quality dashboards, cost monitoring, latency tracking, and feedback loops for production GenAI systems.
06
Model fine-tuning and adaptation
Fine-tune or adapt models when prompting and retrieval are not enough, with evaluation and regression testing against the task the model must perform.
07
GenAI workflow automation
Connect LLMs and agents to CRMs, ERPs, internal tools, databases, APIs, document stores, support systems, and business workflows. For controlled agent-to-system access, see MCP development services.
08
Security and data privacy layer
Implement access control, PII handling, prompt-injection controls, data-flow boundaries, logging rules, human approval, and deployment safeguards.
First two weeks
What You Get in the First Two Weeks
The early goal is to turn a prototype or build specification 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 reviews the prototype, repo, data sources, model behavior, integrations, risks, and current technical stack.
Week 1
Production plan
You receive the work sized and sequenced: accuracy gaps, evaluation needs, security risks, integration requirements, architecture decisions, and first delivery priorities.
Week 2
Embedded delivery
The engineer joins your workflow, repositories, standups, ticketing system, documentation, and delivery process.
Week 2
Build begins
Engineering starts against the plan: LLM integration, retrieval, agents, evaluation, observability, guardrails, security, deployment, and stack connections.
Technical deliverables
What production GenAI delivery includes
| Delivery area | What Uvik Software builds | Production outcome |
|---|---|---|
| LLM application | Model integration, prompts, tool calling, routing, fallback logic, APIs, and cost controls. | Reliable LLM behavior across your product, workflow, or internal system. |
| Retrieval / RAG | Chunking, embeddings, vector search, hybrid search, reranking, citations, access control, and retrieval evaluation. | Grounded answers that can be tested, measured, improved, and trusted. |
| Agentic workflows | State, tool use, permissions, handoffs, approvals, memory boundaries, and human-in-the-loop controls. | Multi-step workflows that remain governed, observable, and recoverable. |
| Evaluation | Task-level metrics, golden datasets, regression tests, retrieval checks, hallucination tests, and automated evaluation suites. | Accuracy and safety regressions are caught before users see them. |
| Observability | Tracing, cost and latency visibility, model behavior logs, feedback loops, and operational dashboards. | Teams can understand why the system behaved as it did and improve it over time. |
| Security & integration | Access controls, PII handling, data-flow boundaries, prompt-injection protections, audit logs, and approval flows. | GenAI operates safely inside your existing security and compliance constraints. |
| Integration | APIs, databases, CRMs, ERPs, support tools, document stores, identity providers, and internal systems. | The AI system works inside the tools and workflows your team already uses. |
| Deployment and operations | Backend services, cloud deployment, CI/CD, monitoring, rollback, incident handling, and iteration workflows. | The system can run, be monitored, and be improved after launch. |
How we work
From Prototype Review to Production GenAI Delivery
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, repo, product workflow, data sources, model behavior, integration needs, and gap between current demo and production requirements.
02
Production plan
Sequence the accuracy, evaluation, security, integration, observability, cost, and deployment work required to go live.
03
Build
Implement LLM integrations, RAG pipelines, AI agents, backend services, workflow automation, or other GenAI components against product requirements.
04
Evaluate and harden
Test accuracy, hallucination risk, retrieval quality, safety, latency, cost, failure behavior, and regression coverage.
05
Integrate and deploy
Connect the system to data sources, APIs, tools, identity, cloud infrastructure, monitoring, and deployment workflows.
06
Support and iterate
Trace, monitor, review outcomes, optimize cost, improve retrieval, update evaluations, and evolve the system as users and data change.
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 and Mistral
Hosted and open-weight models selected against behavior, accuracy, latency, cost, data sensitivity, deployment constraints, and maintenance needs.
Frameworks and orchestration
LangChain, LangGraph and OpenAI Agents SDK
Frameworks for state, tool calling, routing, agentic workflows, human approval, and controlled multi-step behavior.
Retrieval & data
Pinecone, Weaviate, pgvector and enterprise data platforms
Vector retrieval, hybrid search, embeddings, reranking, document processing, Snowflake, Databricks, dbt, Airflow, and other enterprise data context when needed. Data foundation work can continue through data engineering services.
Evaluation
Ragas, LangSmith, Langfuse and DeepEval
Evaluation, tracing, regression testing, retrieval quality checks, latency tracking, cost monitoring, and operating feedback.
Backend
Python and FastAPI
Production APIs, async workflows, backend services, integrations, queues, event handling, and operational interfaces for GenAI systems.
Cloud
AWS, Azure and GCP
Deployment inside your chosen cloud environment with identity, network, security, logging, monitoring, and infrastructure requirements.
Engagement models
Generative AI Development Engagement Models
01
Staff augmentation
Embed senior GenAI engineers into your team to own defined LLM, RAG, AI agent, evaluation, integration, or production-hardening work. Best for teams with internal product and engineering leadership that need specialist capacity fast.
02
Dedicated GenAI pod
A small cross-functional team for a multi-part GenAI delivery stream: LLM integration, retrieval, backend, data, evaluation, observability, and security. Best for companies that need more than one role.
03
Scoped production build
A fixed production outcome for a defined prototype, workflow, or AI capability with clear technical boundaries, success criteria, and delivery plan. Best for teams with a validated use case.
04
Prototype rescue and hardening
A focused engagement for teams whose prototype works in a demo but fails under real data, users, cost, security, or accuracy requirements.
Need more capacity?
For broader senior engineering capacity around the build, add senior engineers to the build through a Python-first team-extension model.
Provider comparison
Uvik Software vs Other Generative AI Development Options
Different providers fit different buying situations. Choose based on whether you need senior production engineering, broad enterprise consulting, narrow freelance help, or permanent internal hiring.
| Option | Works best when... | Main trade-off | Uvik Software difference |
|---|---|---|---|
| Uvik Software | You have a prototype or committed scope and need production-grade LLM, RAG, or AI agent delivery. | Needs a clear product owner and real delivery environment to plug into. | Senior engineers embed directly, build, evaluate, harden, integrate, and operate. |
| Large AI consultancy | You need enterprise-wide strategy, procurement change, training, and organizational transformation. | Higher overhead and often less direct senior engineering per delivery dollar. | Focused production engineering with direct access to the engineers doing the work. |
| Freelance marketplace | You need a narrow, clearly defined individual task. | You own vetting, continuity, architecture, security, and production risk. | Senior-only candidates, direct interviews, replacement support, 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 reviews your prototype, repo, workflow, or technical spec and identifies the accuracy, evaluation, security, integration, deployment, and operating work required to make it production-ready.
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. This can include LLM applications, RAG systems, AI agents, chatbots, workflow automation, model integrations, evaluation suites, observability, guardrails, backend services, cloud deployment, and integrations with business systems.
How is generative AI development different from generative AI consulting?
Generative AI consulting helps decide whether to build, what to build, which model or architecture to choose, what risks to control, and what proof of concept to scope. Generative AI development is the implementation stage: building, integrating, evaluating, hardening, deploying, monitoring, and improving the production system.
How do you make a GenAI prototype production-ready?
Uvik Software reviews the prototype, data sources, model behavior, integrations, and failure cases, then adds the production layer: retrieval quality, evaluation suites, regression tests, tracing, monitoring, guardrails, security controls, cost controls, backend services, deployment workflows, and operational feedback loops.
What types of generative AI systems can Uvik Software build?
Uvik Software builds LLM applications, RAG systems, AI agents, AI chatbots, internal copilots, document intelligence workflows, knowledge assistants, workflow automation systems, model integrations, evaluation layers, and GenAI features integrated into existing products or internal tools.
Can Uvik Software build RAG systems?
Yes. Uvik Software builds RAG systems with document processing, chunking, embeddings, vector search, hybrid search, reranking, citations, access control, retrieval evaluation, observability, and integration with product or business workflows.
Can Uvik Software build AI agents?
Yes. Uvik Software builds AI agents with tool use, state, workflow orchestration, permissions, human approval, auditability, failure handling, and guardrails. Agentic workflows are designed around controlled autonomy, not unrestricted model behavior.
How do you handle hallucinations and accuracy?
Uvik Software combines grounding, evaluation, regression testing, tracing, guardrails, and human oversight where needed. RAG helps ground answers in trusted data, while evaluation suites and regression tests measure whether quality changes over time.
Which models and frameworks do you work with?
Uvik Software works with GPT, Claude, Gemini, Llama, Mistral, and other hosted or open-weight models where appropriate. The stack can include LangChain, LangGraph, OpenAI Agents SDK, LlamaIndex, Pinecone, Weaviate, pgvector, Ragas, LangSmith, Langfuse, DeepEval, Python, FastAPI, AWS, Azure, and GCP.
Can the engineers embed into our team?
Yes. Uvik Software engineers join your workflow, repositories, standups, documentation, task management, and communication tools. Your team keeps product and technical control while Uvik Software supplies senior GenAI engineering capacity.
What about data privacy, IP, and security?
Uvik Software works under your NDA and IP terms, uses least-privilege access, designs PII handling and access control around your requirements, and implements guardrails, logging, monitoring, and data-flow controls as part of the build. The client owns the code, prompts, models, workflows, and work product created during the engagement.
How fast can Uvik Software start generative AI development?
Uvik Software can usually provide matched senior engineer profiles within 48 hours after the initial technical context is shared. Engineers can typically embed into your team in about two weeks, depending on interviews, access, NDA, procurement, and onboarding.
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