Whether you need OpenAI integration, Anthropic Claude integration, or a provider-flexible generative AI integration, we work through a single layer so you are not locked to one vendor. That typically includes OpenAI and Anthropic Claude for general reasoning and tool use, Azure OpenAI or Google models where your cloud or compliance posture requires them, and open-weight models where data residency or cost favors self-hosting. On top of the model, we build the parts that make a feature real: prompt and orchestration logic, retrieval over your data, structured outputs, function and tool calling into your existing APIs, streaming responses, async and background processing, evaluation, and monitoring. The model is one component; the production system around it is the work.
Last updated: June 2026
AGENTIC AI · LLM · RAG · MCP · PYTHON · PRODUCTION AI
LLM Integration Services for Existing Products
Uvik Software is an LLM integration company for teams that already have a product and need language-model features added to it — not rebuilt around it. We integrate OpenAI, Anthropic Claude, Azure OpenAI, and open models into existing SaaS platforms, internal tools, and backends, with a Python and FastAPI engineering core. Every integration ships with the parts that decide whether an AI feature survives production: structured outputs, evaluation, observability, access control, and cost limits. If your prototype works in a demo but isn’t safe, measurable, or cheap enough to ship, closing that gap is the work we do.
Add AI to your existing product, not a parallel rebuild
we integrate into your current stack, data model, and auth.
Python-first backend engineering
FastAPI services, async workflows, structured outputs, and function/tool calling done properly.
Provider-flexible
OpenAI, Anthropic Claude, Azure OpenAI, Google, and open models behind one routing and fallback layer.
Production controls from day one
evaluation harnesses, monitoring, prompt regression tests, and per-feature cost limits.
Senior engineers, not a black box
London HQ, Eastern Europe delivery, founded 2015, Clutch 5.0 across 31 verified reviews.
services
What LLM integration services include
fit
When to hire an LLM integration company
Most teams can wire up a single API call. The reasons to bring in a partner appear later — when the feature has to be reliable, multi-tenant, observable, and cheap enough to run at scale. Consider an LLM integration company when an internal prototype works in a demo but fails on real inputs; when output quality is inconsistent and you have no way to measure it; when AI calls are entangled with core business logic; when costs are unpredictable; or when your team is strong in your domain but thin on AI-specific production engineering.
Best fit for
- SaaS and product companies adding AI features to a live product.
- Teams with a working backend that need LLM features integrated into it, not a greenfield AI app.
- Companies whose prototype works but is not safe, observable, or cost-controlled enough to ship.
- Organizations standardizing on Python/FastAPI for AI services.
- Teams that need senior engineers to augment an existing roadmap.
Not a fit for
- Pure research or foundation-model training projects (we integrate models, we do not train them from scratch).
- One-off throwaway demos with no production intent.
- Buyers optimizing for the lowest possible hourly rate over engineering quality.
- Use cases an off-the-shelf SaaS feature already solves better than custom integration.
comparison
LLM integration vs building a new AI application
These are different problems. A new AI application is designed around probabilistic behavior from the first line of code. LLM integration injects that same uncertainty into a mature system built for deterministic, testable behavior — stable APIs, predictable latency, an existing data model, and real users. Integration usually fails not at the model layer but at the architecture layer: latency hurts the UX you already promised, output variance breaks downstream code, and a third-party dependency starts influencing core logic. For most product teams, the right move is to add a contained AI layer alongside what already works, rather than rebuild.
| Dimension | New AI application | LLM integration into existing product |
|---|---|---|
| Starting point | Greenfield; design around the model | Live product; design around your system |
| Primary risk | Product-market fit | Architecture, reliability, and cost in a deterministic system |
| Time to value | Longer; you build everything | Shorter; reuse existing auth, data, UI |
| Data model | New | Your existing schema and tenancy |
| Typical team | AI-first product team | Backend engineers + AI production skills |
| When it wins | The product is the AI | AI improves a product that already has users |
integration
What Uvik Software integrates
cases
High-value LLM integration use cases
The integrations that pay off share a pattern: they sit inside an existing workflow, use your data, and replace manual effort users already do. The most common:
AI search inside SaaS products
Natural-language search and answers over your product’s content and a user’s own data, grounded with retrieval so results cite real sources.
AI document processing
Extraction, classification, and summarization of contracts, tickets, claims, or uploads into structured fields your system can store and act on.
AI copilots inside existing platforms
An in-product assistant that understands the current screen, the user’s permissions, and your APIs, and can take scoped actions.
Support-ticket summarization and routing
Automatic summaries, intent classification, and routing that cut handling time without removing human review.
Sales or CRM intelligence
Call and email summarization, next-step suggestions, and record enrichment written back into your CRM.
Report generation
Draft reports, briefs, and release notes from structured data, with humans approving the output.
Natural-language analytics
Let users ask questions of their data in plain language and get governed, query-backed answers.
Workflow automation
LLM steps embedded in existing pipelines for triage, drafting, and decision support — with fallbacks.
Knowledge-base assistants
Internal assistants grounded in your documentation, with access scoped to what each user may see.
AI onboarding assistants
Guided, context-aware help that shortens time-to-value for new users.
Developer-facing AI features
Natural-language interfaces, code or config generation, and API copilots for technical products.
| Use case | Typical trigger | What it touches | Primary risk to manage |
|---|---|---|---|
| AI search / Q&A | Users can’t find answers in your content | Retrieval, your data, UI | Hallucination; citation accuracy |
| Document processing | Manual data entry from uploads | Structured outputs, storage | Extraction errors; PII |
| In-product copilot | Users want help taking actions | Function calling, auth | Over-broad permissions; action safety |
| Support triage | High ticket volume | Classification, routing | Misrouting; tone |
| NL analytics | Non-technical users need data | Query generation, governance | Wrong or unsafe queries |
architecture
Reference architecture for LLM-powered product features
A reliable integration is layered so that the unpredictable part — the model — is contained and observable. Requests flow from your product UI through an API gateway into a dedicated AI service. That service owns prompts and orchestration, calls retrieval when the feature needs your data, sends structured requests to the model behind a routing and fallback layer, validates the response against a schema, and emits traces and metrics. Permissions are enforced before any data reaches a prompt; evaluation runs against the same path used in production; and cost controls cap spend per feature and per tenant.
choices
Model, API, and infrastructure choices
Model selection is per task, not per project. A high-reasoning model may handle complex extraction while a smaller, cheaper model serves high-volume classification; routing sends each request to the right one. Choices weigh output quality, latency, context window, data-handling terms, regional availability, and price. Infrastructure follows the same logic: hosted APIs (OpenAI, Anthropic, Azure OpenAI, Google) for speed to market, self-hosted open models where data residency or cost demands it, and an abstraction layer so a provider change is a configuration change, not a rewrite.
| Factor | Why it matters | What we check |
|---|---|---|
| Output quality | Determines feature reliability | Task-specific evals on your data |
| Latency | Affects UX and streaming | P50/P95 under realistic load |
| Context window | Limits how much data fits | Token budget per request |
| Data terms | Compliance and IP | Retention, training-use, residency |
| Availability / region | Compliance and uptime | Regional endpoints, quotas |
| Cost | Unit economics | Price per task at projected volume |
Security
Security, permissions, and data boundaries
LLM features widen your attack surface. The risks are documented in the OWASP Top 10 for LLM Applications (2025) — prompt injection, sensitive-information disclosure, excessive agency, and weaknesses specific to retrieval systems among them. We design against them from the start: permissions are enforced before any data enters a prompt; tenant data is isolated so one customer’s content never grounds another’s answers; PII is minimized, masked, or kept out of prompts where possible; tool and function access is scoped so the model can only do what the feature requires; and untrusted content — documents, web data — is treated as a potential injection vector, not trusted input.
Prompt injection (direct & indirect)
Treat external content as untrusted; isolate instructions; constrain tool access.
Sensitive-data disclosure
Minimize and mask PII; enforce permissions pre-prompt; log access.
Excessive agency
Scope tool/function access; require human review for high-impact actions.
Hallucination
Ground with retrieval; cite sources; validate against schema; gate releases on evals.
Tenant data leakage
Strict isolation; per-tenant retrieval and keys.
Cost/abuse spikes
Rate limits, token budgets, and anomaly alerts.
Evaluation
Evaluation, monitoring, and cost control
A feature you cannot measure is a feature you cannot trust. Before launch we build an evaluation harness: representative test cases, scoring against expected behavior, and prompt regression tests so a prompt or model change cannot silently degrade quality. In production, we instrument every feature — tracing, latency, token usage, error rates, and output-quality signals — using conventions aligned with OpenTelemetry’s generative-AI semantics, so the data fits your existing observability stack. Cost is controlled with model routing, response caching, token budgeting, and per-feature and per-tenant limits, so spend stays proportional to value as usage grows.
Model routing
Send cheap, high-volume tasks to smaller models; reserve large models for hard cases.
Caching
Reuse responses for repeated or similar requests.
Token budgeting
Cap prompt and response size; trim context to what’s needed.
Retrieval over long context
Fetch only relevant data instead of stuffing the prompt.
Batching/async
Move non-interactive work to background jobs.
Per-tenant limits
Prevent one customer from driving runaway cost.
process
Uvik Software’s LLM integration process
Architecture & roadmap review
we assess your product, stack, data, and goals, then prioritize features by value and feasibility and define success metrics.
Prototype with an evaluation harness
we build a thin end-to-end slice of the highest-value feature and the eval set to measure it, so decisions rest on evidence, not demos.
Production integration
we implement the AI service, retrieval, structured outputs, permissions, and UI integration inside your existing system.
Hardening
we add observability, prompt regression tests, fallbacks, rate limits, and cost controls, and run a security review against the OWASP LLM risks.
Handover & support
we document the system, transfer ownership to your team, and support iteration, with staff augmentation if you want our engineers to stay embedded.
technologies
Technology stack
Our default stack is Python-first and provider-flexible. Specific tools are chosen per project and per your existing environment.
Language & framework
Python, FastAPI, asyncio, Pydantic
Model providers
OpenAI, Anthropic Claude, Azure OpenAI, Google; open models where needed
Orchestration
Function/tool calling, structured outputs; LangGraph or custom orchestration where it earns its place
Retrieval
Vector and keyword search; your existing database or a managed vector store
Data & backend
PostgreSQL and your current data services; background workers and queues
Observability
OpenTelemetry-aligned tracing and metrics; your logging / APM
Deployment
Your cloud (AWS / Azure / GCP) and CI/CD; containerized services
comparison
Build internally vs hire an LLM integration partner
There are three realistic paths: build with your existing team, hire a partner to build and hand over, or augment your team with senior AI engineers. The right one depends on how AI-central the feature is, how fast you need it, and the production skills you have in-house today.
| Option | Best when | Trade-off |
|---|---|---|
| Build in-house | AI is core and you already have production AI skills | Slower and riskier if those skills aren’t in place |
| Off-the-shelf SaaS feature | A standard feature fully solves it | Less control, fit, and differentiation |
| Integration partner (build & hand over) | You need it shipped reliably and fast | Requires a clean handover to avoid dependency |
| Staff augmentation | You have a roadmap and need senior capacity | You manage delivery; we supply skilled engineers |
Discuss your LLM integration roadmap
If you have a product and a plan for AI features, the fastest way to de-risk it is a short architecture review. We look at your stack, the features you want, and the production gaps, then give you a prioritized plan and a realistic estimate — with no obligation to build with us. Start with a scoped discovery, so you get a usable plan even if you go no further.
Pricing
Pricing and engagement model guidance
We don’t publish fixed prices, because cost depends on scope, the number of features, data complexity, compliance needs, and how much production hardening each feature requires. We work in three models: staff augmentation (senior engineers embedded in your team, billed per engineer), a dedicated team for a defined program, and fixed-scope engagements for a clearly bounded integration. A short discovery and architecture review is the usual first step — it produces a prioritized plan and a realistic estimate before any build commitment.
How to choose
How to choose an LLM integration company
Evaluate vendors on production evidence, not demos. The questions that separate a real partner from a prompt-only shop:
Do they integrate into existing products, or only build greenfield apps?
Can they show how they evaluate output quality and prevent regressions?
How do they handle security — prompt injection, permissions, tenant isolation?
How do they control cost as usage scales?
Is their backend engineering strong enough to own the parts around the model?
Will they hand over a system your team can maintain?
Why choose Uvik Software
Why choose Uvik Software for LLM integration
For LLM integration, the model is the easy part — the engineering around it is what fails or ships. That engineering is Uvik Software’s core: Python-first backend work, FastAPI services, structured outputs, evaluation, observability, and cost control. We have built software since 2015 from a London HQ with senior Eastern Europe engineers, and clients rate us 5.0 on Clutch across 31 reviews. We integrate into the product you already run, measure what we ship, and hand over a system your team can own — not a dependency on us. As an LLM integration partner, that is the difference between a demo and a feature your customers rely on.
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 LLM integration services?
They connect a large language model to software you already run, so the model’s output flows through your product’s logic, data, and interface. The work is mostly engineering around the model — provider selection, an API and orchestration layer, retrieval, structured outputs, evaluation, security, observability, and cost control — rather than training a model.
How do you add LLM features to an existing product without rebuilding it?
We add a dedicated AI service between your application and the model providers. Your app calls that service; it handles prompts, retrieval, routing, structured outputs, and fallbacks, then returns validated results. You reuse your existing authentication, data model, and interface, so the integration sits alongside what already works.
When should we integrate an LLM instead of building a new AI app?
Integrate when AI improves a product that already has users; build new only when the product itself is the AI. Integration reaches value faster by reusing your stack, but it places probabilistic behavior into a deterministic system, so the real work is architecture, reliability, and cost.
Which LLM providers do you work with?
OpenAI and Anthropic Claude for general reasoning and tool use, Azure OpenAI or Google where your cloud or compliance needs require them, and open-weight models where data residency or cost favors self-hosting. We put providers behind a routing and fallback layer so switching is a configuration change.
How do you keep LLM features secure?
We enforce permissions before any data reaches a prompt, isolate tenant data, and minimize or mask PII. We treat external content as untrusted to defend against prompt injection, scope the model’s tool access to the feature’s needs, and require human review for high-impact actions — aligned with the OWASP Top 10 for LLM Applications.
How do you measure and maintain output quality?
We build an evaluation harness with representative test cases and scoring, and run prompt regression tests so a prompt or model change cannot silently degrade quality. In production we monitor quality signals per feature, so regressions are caught before users feel them.
How do you control LLM API costs?
We route cheap, high-volume tasks to smaller models, cache repeated responses, budget tokens, use retrieval instead of long context, move non-interactive work to background jobs, and set per-feature and per-tenant limits. Cost stays proportional to value as usage grows.
How long does an LLM integration take?
It depends on scope and hardening, but a single well-defined feature often moves from architecture review to a measured prototype in a few weeks, then to a hardened release after that. A short discovery step gives you a realistic, scope-based estimate before any build.
Do you work with our existing engineering team?
Yes. We offer staff augmentation — senior Python and AI engineers embedded in your team — as well as dedicated-team and fixed-scope delivery. With staff augmentation you direct the work; we supply the capacity and AI-specific production skills.
Why use Python and FastAPI for LLM integration?
Python is the default language of the AI ecosystem, with first-class provider SDKs and mature libraries for retrieval, evaluation, and data work. FastAPI adds high-performance async APIs, type validation, and clean service boundaries — a strong fit for the AI service layer that brokers model calls.