Timeline. Scope varies, but a focused single-source build typically reaches production faster than a multi-source system with strict access control and custom evaluation. We size the timeline during the architecture review rather than guess up front.
Last updated: June 2026
Building with LangChain · LangGraph · MCP
50+ senior engineers
GDPR-aware security under NDA
Founded 2015 Python-first delivery
RAG · VECTOR SEARCH · LLM · PYTHON · ENTERPRISE KNOWLEDGE
RAG Development Services for Enterprise Knowledge Systems
Your RAG demo works. Production is where it breaks — wrong citations, permission leaks, and answers that drift as documents change. Uvik Software is a Python-first engineering partner that builds, secures, and productionizes enterprise RAG systems: the retrieval-augmented generation behind “chat with your documents,” internal knowledge assistants, and grounded AI search. Founded in 2015 and headquartered in London, with senior engineering talent across Eastern Europe, we pair LLM application work with the data-pipeline and backend depth that production retrieval actually demands — moving a promising proof-of-concept into a reliable, evaluated, access-controlled system the business can trust with real users and real questions.
5.0
Clutch rating across verified reviews.
2015
Founded as a Python-first engineering company.
5+ years
Engineer experience floor. No juniors. No freelancers.
+72 NPS
Client NPS, rolling 12 months. Published openly.
What you get
What you get with Uvik Software
1
Production RAG, not prototypes
ingestion, retrieval, generation, and evaluation engineered as one dependable pipeline.
2
Python-first backends
FastAPI services, clean data pipelines, and secure integrations built to your engineering standards.
3
Retrieval quality you can measure
hybrid search, reranking, and evaluation datasets that score accuracy before launch, not after complaints.
4
Security by design
permission-aware retrieval, audit logging, and data that stays inside your infrastructure.
5
Flexible engagement
a scoped delivered project, or senior engineers embedded directly in your team.
What include
What RAG development services include
RAG development services cover the end-to-end work of turning your documents and data into a grounded, queryable AI system. The scope spans designing the retrieval architecture, building ingestion and chunking pipelines, selecting and configuring a vector database, implementing hybrid search and reranking, engineering the generation and citation layer, wiring secure access controls, and standing up evaluation and observability so quality is measured rather than assumed. At Uvik Software this is delivered as production Python services — typically FastAPI — with the data engineering and backend integration that enterprise retrieval depends on.
RAG service scope at a glance:
| Service area | What it covers | Typical deliverable |
|---|---|---|
| RAG architecture & strategy | Use-case scoping, retrieval design, model and vector-DB selection, cost/latency targets | Architecture doc + decision record |
| Data ingestion & pipelines | Connectors, parsing, chunking, metadata extraction, sync/versioning | Automated ingestion pipeline |
| Retrieval engineering | Embeddings, vector search, hybrid (BM25 + vector), reranking, metadata filtering | Tuned retrieval layer |
| Generation & grounding | Prompt design, context assembly, citation generation, confidence/refusal logic | Grounded answer service |
| Backend & integration | FastAPI services, auth, rate limiting, API/SDK, app and chat integration | Production backend |
| Evaluation & QA | Eval datasets, retrieval & answer scoring, regression testing | Evaluation harness + report |
| Security & access control | Permission-aware retrieval, audit logs, PII handling, data residency | Access-control design |
| Observability & run | Tracing, monitoring, cost dashboards, drift and quality alerts | Observability stack |
| RAG rescue / improvement | Diagnosing and fixing failing or stalled RAG systems | Remediation plan + fixes |
hire
When to hire a RAG development company
Most teams can stand up a basic RAG demo in a week. The difficulty is everything after the demo: accuracy on real questions, security, scale, and maintainability. Consider a RAG development partner when one or more of these is true.
01
Demo works, real questions fail
Your LLM pilot looks great in a scripted demo but hallucinates, or returns nothing useful, on real user questions.
02
Knowledge is scattered
Knowledge is scattered across PDFs, wikis, ticketing systems, SharePoint, and databases with no unified retrieval layer.
03
Retrieval quality is inconsistent
Retrieval quality is inconsistent and you have no way to measure whether a change actually improved answers.
04
Permission-aware answers are required
You need permission-aware answers, so each user only sees content they are cleared to access.
05
Prototype needs productionization
A prototype must become a secure, monitored, maintainable production system with an owner and a runbook.
06
Team capacity is limited
You lack in-house Python/AI engineers, or your team is strong but at capacity and the timeline is fixed.
comparison
RAG vs enterprise search vs chatbot vs fine-tuning
These four approaches are routinely confused, and choosing the wrong one wastes budget. RAG retrieves relevant content at query time and grounds a language-model answer in it, with citations. Enterprise search returns documents, not answers. A scripted chatbot follows predefined flows. Fine-tuning changes model weights to teach style or behaviour — it does not give the model live access to your data. Many production systems combine RAG for knowledge with light fine-tuning for tone or format.
| Dimension | RAG | Fine-tuning | Enterprise search | Scripted chatbot |
|---|---|---|---|---|
| What it does | Retrieves your data and grounds an LLM answer | Retrains model weights on examples | Returns ranked documents | Follows predefined dialog flows |
| Knowledge freshness | Live — reflects updates immediately | Stale — fixed at training time | Live (index-dependent) | Static unless rebuilt |
| Natural-language answers | Yes, with citations | Yes | No — links/snippets | Limited, templated |
| Source traceability | High — citations to documents | Low — opaque weights | High — the document itself | Low |
| Update cost | Low — re-index content | High — retrain + eval | Low — re-crawl | Medium — author flows |
| Best for | Grounded answers from changing knowledge | Teaching tone, format, narrow skills | Finding documents | Deterministic, narrow tasks |
| Main risk | Poor retrieval → wrong/unsupported answers | Drift, cost, overfitting | No synthesis; user still reads | Brittleness; can’t generalize |
build
What Uvik Software builds
Uvik Software builds production retrieval systems and the backends around them — not slideware.
Typical builds:
use cases
Enterprise RAG use cases
RAG earns its cost where accurate answers from your own data are expensive to get by hand. The highest-ROI patterns are usually found in teams that work with large document collections, changing internal knowledge, compliance-heavy processes, or support workflows where every answer needs a source.
01
Chat with documents
Q&A over contracts, manuals, reports with citations. This use case lets teams ask natural-language questions and receive grounded answers linked back to the source document, section, or passage. It is useful when employees spend too much time searching through long PDFs, policies, technical manuals, or reports manually.
Who buys it: Ops, legal, knowledge teams.
02
Internal knowledge assistants
Answers staff questions from policies and docs, permission-aware. These assistants help employees find internal procedures, HR policies, IT documentation, onboarding materials, and company knowledge without opening multiple systems. The key requirement is access control, so each employee only sees answers based on documents they are allowed to access.
Who buys it: IT, HR, enablement.
03
Customer support knowledge systems
Grounds agent/bot answers in help docs and past tickets. A RAG system can support human agents or power customer-facing bots by retrieving the most relevant help articles, product documentation, and historical resolutions. This improves answer consistency, reduces escalation load, and helps support teams respond faster without inventing unsupported answers.
Who buys it: Support, CX.
04
Legal and compliance document search
Surfaces clauses, precedents, and obligations with sources. Legal and compliance teams can use RAG to search across contracts, policies, filings, audit documents, and regulatory guidance. The system must provide traceable citations, preserve context, and support review workflows because unsupported or incomplete answers create real risk.
Who buys it: Legal, compliance, risk.
05
Healthcare or insurance document workflows
Retrieves from guidelines, policies, and records with audit trails. In regulated environments, RAG can help teams find relevant policy language, clinical guidelines, claim rules, case documents, or internal procedures. The value comes from combining accurate retrieval, strict permissions, clear source attribution, and logs that make the workflow auditable.
Who buys it: Regulated enterprises.
06
Sales enablement assistants
Answers reps from playbooks, pricing, and product docs. Sales teams can use RAG to quickly find approved messaging, competitive notes, pricing guidance, product capabilities, and proposal content. This helps reps answer buyer questions faster while keeping responses aligned with the latest approved materials.
Who buys it: Revenue, sales ops.
07
Developer knowledge assistants
Searches codebases, ADRs, and runbooks for engineers. Engineering teams can use RAG to retrieve information from technical documentation, architecture decision records, incident reports, API docs, and operational runbooks. This reduces onboarding time, speeds up debugging, and helps developers understand complex systems without interrupting senior engineers.
Who buys it: Engineering, platform.
08
Research and analyst copilots
Synthesizes findings across reports and filings with attribution. Analysts can use RAG to explore financial filings, market reports, research notes, internal documents, and competitive intelligence. The system helps summarize patterns across many sources while keeping attribution visible, so findings can be checked before they influence decisions.
Who buys it: Research, finance, strategy.
Chat with documents
Internal knowledge assistants
Customer support knowledge systems
Legal and compliance document search
Healthcare or insurance document workflows
Sales enablement assistants
Developer knowledge assistants
Research and analyst copilots
architecture
Reference architecture for production RAG systems
A production RAG system is a pipeline of distinct layers, each with its own failure modes. A weekend prototype skips most of them; an enterprise
chunking
Data ingestion, chunking, indexing, and retrieval design
Why naive RAG fails. “Embed everything and search” works in a demo and disappoints in production. Chunks are split arbitrarily and lose context; retrieval returns plausible-but-irrelevant passages; the model is handed too much low-signal context and important facts get buried; there is no filtering, so a user retrieves documents they should never see; and nobody can tell whether the system is getting better or worse. Research on long-context models shows answer quality degrades when relevant information sits in the middle of a long context, which is why precise retrieval and reranking matter more than simply stuffing the prompt.
01
Chunking strategy
Chunk size and overlap are tuned to the document type — a 200-page manual is treated differently from a one-page policy. Where structure exists, such as headings, sections, tables, lists, and document hierarchy, structure-aware chunking preserves meaning instead of cutting content mid-thought. For enterprise RAG, chunking is not a one-time technical setting; it is part of retrieval design. The goal is to create chunks that are small enough to retrieve precisely, but rich enough to carry the context needed for a grounded answer.
02
Metadata & filtering
Every chunk carries metadata — source, owner, date, document type, sensitivity — so retrieval can filter by recency, department, geography, customer, product line, or entitlement before ranking. Metadata is also the backbone of permission-aware retrieval. Without it, the system may retrieve the right text for the wrong user, which is unacceptable in enterprise environments. Good metadata makes retrieval more accurate, easier to debug, and safer to operate across teams, tenants, and access levels.
03
Hybrid search & reranking
Pure vector search misses exact terms, IDs, product names, acronyms, error codes, and legal clauses. Hybrid retrieval combines semantic vectors with keyword search, such as BM25, so the system can match both meaning and exact language. A cross-encoder reranker then reorders candidates so the most relevant context reaches the model first. This combination is one of the biggest levers on answer quality because it reduces noise before generation starts.
04
Citation grounding & refusal
Answers attribute claims to specific source chunks, and the system is engineered to say “I don’t have enough information” rather than guess when retrieval is weak. This matters because enterprise users do not just need fluent answers — they need answers they can verify. Citation grounding makes the system useful for legal, compliance, support, research, and internal knowledge workflows where every important claim should be traceable back to the source.
05
Stale knowledge & versioning
Ingestion stays in sync as sources change, supersedes outdated content, and tracks document versions so answers reflect current truth, not last quarter’s policy. A production RAG system needs to know when a document was updated, when it was replaced, and which version should be used in retrieval. Versioning prevents old policies, expired contracts, outdated product information, or deprecated procedures from continuing to influence answers after they should no longer be trusted.
06
Retrieval evaluation & continuous improvement
Retrieval quality is measured with test sets, expected answers, relevance scoring, and production traces. This gives teams a way to know whether a change to chunking, embeddings, filters, reranking, or prompts actually improved the system. Evaluation also helps identify recurring failure modes: missing sources, weak chunks, poor metadata, bad ranking, or answers that rely on unsupported context. Without this layer, every update becomes guesswork; with it, the RAG system can improve safely over time.
search infrastructure
Vector databases and search infrastructure
There is no single “best” vector database — the right choice depends on scale, latency, existing infrastructure, and operational preference. Uvik Software selects and configures the option that fits your stack rather than defaulting to one vendor.
pgvector (PostgreSQL)
Best fit: Teams already on Postgres; moderate scale.
Notes: Keeps data in one DB; simple ops.
pgvector is often the right choice when the team already runs PostgreSQL and wants to avoid introducing a separate vector infrastructure layer too early. It keeps application data, metadata, permissions, and embeddings close together, which simplifies operations, backups, access control, and developer workflows.
Qdrant
Best fit: High-throughput production; strong filtering.
Notes: Open-source; self-host or managed.
Qdrant is a strong option for production RAG systems that need fast vector search, metadata filtering, and operational flexibility. It works well when retrieval performance matters and the team wants the choice between self-hosting and using a managed service.
Pinecone
Best fit: Large scale; fully managed, low-ops.
Notes: Purpose-built; usage-based cost.
Pinecone is useful when teams want a managed vector database with minimal infrastructure overhead. It is often a fit for large-scale retrieval workloads where operational simplicity, reliability, and managed scaling are more important than running infrastructure in-house.
Weaviate
Best fit: Hybrid search + modules; flexible schemas.
Notes: Open-source or managed.
Weaviate fits teams that need flexible schemas, hybrid retrieval, and a broader search platform around vector search. It can support more advanced retrieval setups where semantic search, metadata, modules, and structured data need to work together.
Chroma
Best fit: Prototyping and smaller datasets.
Notes: Lightweight; fast to start.
Chroma is a practical choice for early RAG prototypes, experiments, and smaller datasets. It helps teams move quickly when validating retrieval flows, chunking strategies, and application logic before deciding whether a more production-oriented vector database is needed.
Elasticsearch / OpenSearch
Best fit: Existing search estate; strong BM25 + vectors.
Notes: Good for hybrid in incumbent stacks.
Elasticsearch and OpenSearch are strong options when an organization already has search infrastructure in place. They are especially useful for hybrid search, where keyword matching, filters, document metadata, and vector retrieval need to work together inside an existing enterprise search stack.
quality control
RAG evaluation, observability, and quality control
Evaluation is what separates a production system from a prototype — and it is where most vendors are thin. Uvik Software ships RAG with an evaluation harness so quality is a number you can track, not a feeling. We build an evaluation dataset from your real questions, score the system before launch, and re-score on every change to catch regressions.
| Metric | What it measures | Why it matters |
|---|---|---|
| Context recall | Did retrieval fetch the information needed to answer? | Caps the best possible answer quality |
| Context precision | How much retrieved context was actually relevant | Noise degrades the generated answer |
| Faithfulness / groundedness | Is the answer supported by retrieved context? | Direct measure of hallucination |
| Answer correctness | Is the answer right against a known reference? | End-to-end quality |
| Citation accuracy | Do citations point to the right sources? | Trust and auditability |
| Latency & cost | Response time and per-query cost | Production viability and budget |
access control
Security, permissions, and access control
In the enterprise, the question is not only “is the answer correct?” but “is this user allowed to see it?” Uvik Software engineers security into the retrieval path, not as an afterthought.
Permission-aware retrieval
Entitlements are enforced at query time so retrieval only returns chunks a user is cleared to access.
Document-level ACLs & metadata filtering
Access maps from your source systems flow through to the index.
Data residency & isolation
Documents stay in your infrastructure; nothing is used to train third-party models.
PII handling
Detection, redaction, and policy controls for sensitive fields.
Audit logging
Who asked what, what was retrieved, and what was answered — for compliance and review.
Encryption & secrets
Encryption in transit and at rest, with managed secrets and key handling.
Failure modes we engineer against — and the controls that prevent them:
| Failure mode | Symptom | Risk control |
|---|---|---|
| Hallucinated answers | Confident but unsupported claims | Grounding + faithfulness eval + refusal logic |
| Poor retrieval | Right doc exists but isn’t fetched | Hybrid search, reranking, chunking tuning |
| Permission leakage | User sees restricted content | Query-time entitlement filtering + audit |
| Stale knowledge | Answers reflect outdated documents | Sync pipeline + versioning + supersedence |
| Prompt injection | Malicious content alters behaviour | Input/content isolation, allow-lists, guardrails |
| Silent quality drift | Quality drops unnoticed over time | Continuous eval + monitoring + alerts |
| Cost / latency blowout | Slow or expensive at scale | Caching, model routing, retrieval limits |
process
Uvik Software’s RAG development process
A predictable path from “we have documents and an idea” to “we have a measured, secure system in production.”
01
Discovery & RAG architecture review
Defines use cases, data sources, success metrics, and the target architecture. (This is also our recommended low-risk entry point.)
02
Data audit & ingestion design
Assess source quality and access rules; design ingestion, chunking, and metadata.
03
Retrieval build & tuning
Implement embeddings, hybrid search, reranking, and filtering; tune against real questions.
04
Evaluation & hardening
Build the eval set, score retrieval, and answers, add security, permissions, and refusal logic.
05
Production deployment & observability
Ship the FastAPI backend and integration; instrument tracing, monitoring, and alerts.
06
Iteration or embedded team
Ongoing improvement as a retainer, or senior engineers embedded in your team for continued build-out.
Technologies
Technology stack
Uvik Software is Python-first and tool-pragmatic — we choose components that fit your stack and constraints.
Language, Backend / API
Python (primary)
FastAPI (primary)
Django
Flask
LLM orchestration
LangChain
LangGraph
LlamaIndex
Model Context Protocol (MCP)
Models
OpenAI
Anthropic Claude
and open-weight models
Vector databases
pgvector
Qdrant
Pinecone
Weaviate
Chroma
Search & reranking
Hybrid BM25 + vector
cross-encoder and hosted rerankers
Evaluation & Observability
RAGAS and custom evaluation harnesses
OpenTelemetry tracing
LLM tracing/eval tooling
cost dashboards
Data engineering
ETL/ELT
Airflow
dbt
PostgreSQL
cloud object storage
Cloud & deployment
AWS
GCP
Azure
containers and CI/CD
comparison
Build vs buy vs a RAG development partner
Three paths, three trade-offs. Off-the-shelf tools are fast but generic and shallow on security and integration. A pure in-house build gives maximum control but needs scarce Python/AI and data-engineering talent and a long runway. A development partner gives you a production system and senior capacity without a permanent hire — and can hand over or embed.
| Factor | Off-the-shelf tool | In-house build | RAG development partner |
|---|---|---|---|
| Time to production | Fast | Slow | Fast–medium |
| Customization & integration | Limited | Full | Full |
| Security & permission control | Generic | Full (if skilled) | Full |
| Required internal expertise | Low | High | Low–medium |
| Evaluation & reliability | Vendor-defined | Depends on team | Engineered + measured |
| Best for | Simple, low-risk use cases | Core IP with a strong AI team | Production enterprise systems, fixed timelines |
engagement model
Pricing and engagement model guidance
RAG projects vary too much for a single sticker price — cost is driven by the number and messiness of data sources, security and compliance requirements, scale and latency targets, and how rigorous the evaluation needs to be. Rather than quote blind, Uvik Software offers four engagement models so spend matches risk.
1
Architecture review
a short, fixed-scope engagement that produces a target architecture, risks, and a delivery plan. The recommended first step.
2
Project build
a scoped, milestone-based build of a defined RAG system, delivered to production.
3
Staff augmentation
senior Python/AI engineers embedded in your team under your direction, billed monthly.
4
Managed improvement / retainer
ongoing evaluation, tuning, and feature work on a live system.
choose
How to choose a RAG development company
The category is crowded with generalists. Use these criteria to separate teams that build demos from teams that ship production systems.
Questions worth asking any vendor: How do you measure retrieval quality? How do you enforce who can see what? What happens when retrieval fails — does the system guess or refuse? How do you keep answers current as documents change? Who owns the system after launch?
Book a RAG architecture review
If you are building, securing, or rescuing an enterprise RAG system, start with a focused architecture review. You’ll leave with a target architecture, an honest read on your data, and a delivery plan — from a Python-first team that engineers retrieval for production, not just demos.
Why Uvik
Why choose Uvik Software for RAG development
Uvik Software brings the part of RAG that decides whether it works in production: engineering discipline. We are Python-first, with the data-pipeline and backend depth that retrieval depends on, an evaluation-and-observability habit that makes quality measurable, and security designed into the retrieval path. You can engage us for a delivered project or embed our senior engineers directly in your team.
Best fit for
- Enterprises productionizing or securing a RAG / document-AI system.
- Teams whose pilot works in a demo but fails on real questions or at scale.
- Organizations needing permission-aware answers over sensitive documents.
- Product teams that need a reliable Python/FastAPI RAG backend, fast.
- Companies that want senior AI engineers embedded without a permanent hire.
Not a fit for
- A weekend hobby prototype with no production or security needs.
- Use cases better solved by enterprise search or a scripted bot than by RAG.
- Buyers seeking the cheapest possible vendor over a system that works.
- Teams wanting model fine-tuning alone, with no retrieval requirement.
Risk reduction. Start with a fixed-scope RAG architecture review, not a blind build. You get a target architecture, a measured view of your data, and a delivery plan before committing to a full project — so the decision is evidence-based and the downside is contained.
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 RAG development services?
RAG development services design and build retrieval-augmented generation systems that connect a language model to your own data. The work spans ingestion and chunking pipelines, a vector database, hybrid retrieval and reranking, a grounded generation layer with citations, secure access control, and evaluation — typically delivered as production Python/FastAPI services.
How much does enterprise RAG development cost?
Cost depends on the number and messiness of data sources, security and compliance needs, scale and latency targets, and evaluation rigor. Rather than quote blind, Uvik Software offers a fixed-scope architecture review, scoped project builds, monthly staff augmentation, and retainers — so spend matches risk. The architecture review sizes the rest.
How long does a RAG project take?
It varies with scope. A focused single-source system reaches production faster than a multi-source build with strict access control and custom evaluation. Uvik Software sizes the timeline during the architecture review instead of guessing up front.
What is the difference between RAG and fine-tuning?
Fine-tuning retrains model weights to teach tone, format, or a narrow skill; it does not give the model live access to your data and goes stale when data changes. RAG retrieves current information at query time and grounds the answer with citations. Many production systems combine both.
RAG vs enterprise search — which do we need?
Enterprise search returns documents; RAG returns answers grounded in those documents, with citations. If users need to read and decide, search may suffice. If they need direct, sourced answers from changing knowledge, RAG fits. The two are often combined.
How do you reduce hallucinations in a RAG system?
By improving retrieval (hybrid search, reranking, tuned chunking and metadata), grounding answers in retrieved context with citations, engineering refusal when evidence is weak, and measuring faithfulness with an evaluation harness so regressions are caught before release.
How do you secure a RAG system and control access?
Entitlements are enforced at query time so retrieval only returns content a user may see; document-level ACLs and metadata flow from source systems to the index; data stays in your infrastructure and is never used to train third-party models; and audit logs record every query, retrieval, and answer.
Which vector database do you use?
The one that fits your stack — pgvector for teams already on PostgreSQL, Qdrant or Pinecone for high-throughput production, Weaviate for flexible hybrid search, and Chroma for prototyping. Uvik Software selects based on scale, latency, and operational preference rather than defaulting to one vendor.
Why Python for RAG development?
Python is the native language of the AI and data ecosystem — LLM SDKs, LangChain/LangGraph/LlamaIndex, embeddings, and evaluation tooling are Python-first — and FastAPI provides a fast, production-grade backend. A Python-first partner keeps the AI logic, data pipelines, and API layer in one coherent, maintainable stack.
Can you fix or improve an existing RAG system?
Yes. Uvik Software runs RAG rescue engagements: we diagnose why retrieval or grounding underperforms, then fix chunking, hybrid search, reranking, evaluation, security, and reliability — and stand up the observability needed to keep quality from drifting again.