Last updated: June 2026

Building with LangChain · LangGraph · MCP 50+ senior engineers GDPR-aware security under NDA Founded 2015 Python-first delivery

AGENTIC AI · LLM · RAG · MCP · PYTHON · PRODUCTION AI

AI Application Rescue Services

Most AI applications do not fail because the idea was wrong. They fail because a prototype that worked once in a demo was pushed toward real users without the architecture, evaluation and controls that production demands. Uvik Software is a Python-first engineering partner that stabilizes, refactors, productionizes or rebuilds unstable AI and LLM applications, including AI-assisted and vibe-coded prototypes, so they become reliable, observable and safe to scale.

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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.

Senior engineers, not generalists.

Python, FastAPI and AI/ML specialists who take fragile AI builds to production.

A fixed-scope rescue audit.

Root-cause findings across cost, latency, accuracy, reliability and security before any rebuild decision.

A staged plan.

Clear 7-, 14- and 30-day stabilization milestones, so progress is visible from week one.

Honest rebuild-vs-refactor guidance.

We do not default to rewriting everything, and we say so when a refactor is the right call.

Controls that hold up.

Evaluation, observability and security aligned with OWASP and NIST guidance.

At a glance

What AI application rescue means

AI application rescue is the process of diagnosing and fixing an AI or LLM application that is unstable, unreliable, too expensive, insecure or stuck between prototype and production. The work usually spans architecture, retrieval (RAG), prompt and model strategy, evaluation, observability, security and deployment. The goal is a system that behaves predictably under real traffic and can be safely maintained and extended.

It is distinct from ordinary debugging. A single bug has a single fix. A rescue addresses the reasons an application keeps producing bugs, cost surprises and outages: missing structure, no measurement of output quality, no cost or reliability controls, and security gaps specific to language-model systems.

Signs

Signs your AI application needs rescue

Any one of these is a warning. Several together usually mean a prototype reached users before it was ready.

Inconsistent output

The same input produces different or wrong answers, and quality drifts over time.

Rising or unbounded cost

Model spend climbs without a clear ceiling, and no one can attribute it to features.

Slow under load

Responses are acceptable in a demo but degrade badly with real concurrency.

No visibility

There is no tracing, logging or cost breakdown, so failures are diagnosed by guesswork.

No quality measurement

Output quality is judged by eye, with no test set or evaluation gate before release.

Security gaps

Hard-coded secrets, no input or output validation, and exposure to prompt injection.

Fragile codebase

The team avoids changes because small edits break unrelated behavior.

Stuck before launch

The prototype works, but no one is confident putting it in front of customers.

failure

Common AI app failure modes

Most failing AI applications share a small number of root causes. Naming them is the first step to fixing them.

Failure mode	What you experience	Typical root cause	Direction of fix
Hallucination / wrong answers	Confident but incorrect output	Weak retrieval grounding, no validation	Improve RAG, constrain and validate output, add eval gates
Cost blowout	Unpredictable monthly model bills	Oversized model, no caching, bloated context	Right-size models, cache, trim context, add budgets
Latency	Slow responses under real use	Serial calls, large context, no caching	Stream, parallelize, cache, tune retrieval
Unreliability	Intermittent failures and outages	No retries, fallbacks or rate limiting	Add resilience patterns and graceful degradation
Poor RAG quality	Irrelevant or outdated context	Bad chunking, no reranking, stale index	Rework chunking, hybrid search, reranking, freshness
Security exposure	Injection, data leakage risk	No input/output controls, excessive agency	Apply OWASP LLM controls, least privilege
No observability	Cannot tell why it failed	No tracing or metrics	Instrument traces, cost, latency, quality
Unmaintainable code	Risky to change	No structure, tests or docs	Refactor to clear boundaries, add tests and docs

fit

When you need AI application rescue

Best fit for

Teams whose AI prototype works in a demo but is not safe to launch.
Products where LLM cost or latency has become a business problem.
AI features that are live but unreliable, unmonitored or insecure.
AI-assisted or vibe-coded builds that need professional engineering to reach production.
Companies that need senior Python and AI engineers quickly, with or without their own team in place.

Not a fit for

Pure research with no intention of shipping to real users.
Work that requires us to misrepresent reliability, security or compliance status.
Engagements where no access to the codebase, data or stakeholders can be provided.

delivery

What Uvik Software delivers

A rescue engagement is organized into workstreams. Not every project needs all of them; the audit determines which apply and in what order.

Diagnostic audit

What it includes: Architecture, cost, latency, accuracy, reliability and security review.

Outcome: Severity-ranked findings and a plan.

Architecture, API & deployment

What it includes: Clear boundaries, FastAPI services, orchestration design, containers, IaC, CI/CD and staging.

Outcome: A structure the team can maintain, with repeatable, safe releases.

Model & prompt strategy

What it includes: Model selection, prompt and context design, structured output.

Outcome: Predictable, cheaper, more accurate calls.

RAG stabilization

What it includes: Chunking, embeddings, hybrid search, reranking, retrieval eval.

Outcome: Relevant, current, grounded context.

Evaluation & observability

What it includes: Test sets, scoring, regression gates in CI, tracing, cost, latency and quality metrics.

Outcome: Quality measured, not assumed — and failures become visible and debuggable.

Security

What it includes: OWASP LLM controls, validation, least privilege.

Outcome: Reduced injection and data-leak risk.

comparison

Prototype vs production AI architecture

The gap between a working prototype and a production system is where most AI projects stall. Closing it is the core of a rescue.

Purpose

Prototype reality: Prove the idea works once.
Production requirement: Work repeatedly for real users.

A prototype is designed to show that an AI idea is possible. A production system must prove that the same behaviour can happen reliably across different users, inputs, edge cases, and business scenarios.

Inputs & access

Prototype reality: Trusted, manual inputs.
Production requirement: Untrusted inputs, authentication, authorization.

In production, users may submit incomplete, malicious, sensitive, or unexpected inputs. The system needs clear access control, user identity, permissions, and validation before any AI workflow runs.

Errors

Prototype reality: Fail and retry by hand.
Production requirement: Retries, fallbacks, graceful degradation.

A prototype can break during a demo and be restarted manually. A production system needs predictable failure handling: retries, provider fallbacks, timeouts, error messages, and safe degradation when something goes wrong.

Output quality

Prototype reality: Judged by eye.
Production requirement: Measured with evaluation and gates.

Prototype quality is often based on whether a few answers look good. Production quality needs test sets, scoring, regression gates, and measurable thresholds before changes reach users.

Cost

Prototype reality: Ignored.
Production requirement: Right-sized, cached, budgeted, monitored.

In production, every token, model call, retry, and long context affects unit economics. Cost control requires model routing, caching, token budgets, usage limits, and monitoring by feature or tenant.

Observability

Prototype reality: Print statements.
Production requirement: Tracing, metrics, alerting.

Production AI systems need visibility across prompts, model calls, retrieval steps, tools, latency, failures, and output quality. Without observability, teams cannot debug or improve the system safely.

Security

Prototype reality: Out of scope.
Production requirement: Prompt-injection defense, validation, least privilege.

Production AI must defend against prompt injection, data leaks, unsafe tool use, and unauthorized access. Security needs to be designed into the architecture, not added after launch.

Deployment

Prototype reality: Runs on a laptop or one server.
Production requirement: Automated pipeline, staging, rollback.

A production system needs repeatable deployment: CI/CD, environments, staging checks, configuration management, rollback, and release controls so updates can ship safely.

process

The Uvik Software AI rescue audit methodology

Every engagement begins with a fixed-scope audit. It is deliberately structured so you receive a usable deliverable and a clear decision before committing to a larger build.

Intake

We review the codebase, infrastructure, model usage, data flows and the symptoms you are seeing, and agree the questions the audit must answer.

Diagnostic

We assess architecture, retrieval, prompts and models, evaluation, observability, security and deployment, and reproduce the key failures.

Findings & decision

We deliver severity-ranked findings and a rebuild-versus-refactor recommendation with the reasoning, not a default to rewriting.

Stabilization

We execute the prioritized plan against 7-, 14- and 30-day horizons, fixing the highest-severity issues first.

Handover

We leave documentation, tests and observability so your team can operate and extend the system, or retain a smaller Uvik Software team to do so.

We leave documentation, tests and observability so your team can operate and extend the system, or retain a smaller Uvik Software team to do so.

plan

7-day, 14-day and 30-day stabilization plan

Horizons are indicative and set during the audit based on severity. The principle is constant: stop the bleeding first, restore predictability next, then make the system maintainable.

First 7 days

Stop active failures and runaway cost

Representative outcomes: Critical bugs contained, cost ceilings and basic monitoring in place, highest-risk security gaps closed

By 14 days

Restore predictable behavior

Representative outcomes: Reliability patterns added, retrieval and prompt issues addressed, latency reduced, evaluation baseline established

By 30 days

Make it maintainable and observable

Representative outcomes: Tracing and quality metrics live, evaluation gates in CI, security hardened to OWASP guidance, documentation and handover

decision framework

Rebuild vs refactor: a decision framework

Rewriting everything is rarely the fastest route, and rarely the cheapest. The audit weighs concrete signals before recommending a path.

Core data model

Favor refactor:

Favor rebuild

Code structure

Favor refactor: Messy but salvageable

Favor rebuild: No structure, unsafe to change

Security posture

Favor refactor: Fixable in place

Favor rebuild: Insecure by design

Scale headroom

Favor refactor: Adequate with tuning

Favor rebuild: Cannot meet required load

Cost to patch

Favor refactor: Lower than replacement

Favor rebuild: Exceeds rebuild cost

Team familiarity

Favor refactor: Team knows the code

Favor rebuild: No one understands it

Fixing

Fixing LLM cost, latency, hallucination and reliability

These four problems account for most production AI pain. Each has concrete, engineering-led fixes rather than prompt tweaks alone.

Cost

Most overspend comes from using one large model for everything and sending more context than the task needs. Uvik Software right-sizes models per task, caches repeated and semantically similar requests, trims prompts and retrieved context, batches where possible, routes simple work to smaller models, and sets token budgets with spend monitoring so cost becomes predictable.

Latency

Latency is an architecture problem. We stream responses, run independent steps and tool calls in parallel, use smaller fast models for sub-tasks, tune retrieval so less context is fetched, cache frequent results, and add timeouts and circuit breakers so one slow dependency does not stall the whole request.

Hallucination and accuracy

Hallucination cannot be switched off, only reduced and contained. We improve retrieval grounding and source quality, constrain outputs with structured formats and validation, require citations where they matter, add evaluation gates that block quality regressions before release, and keep human review for high-risk actions, consistent with NIST guidance on confabulation.

Reliability

We add the resilience patterns prototypes skip: retries with backoff, fallbacks, idempotency, rate limiting, request queueing, graceful degradation when a provider is slow or down, and error budgets so reliability is managed rather than hoped for.

stabilization

RAG stabilization

Weak retrieval is one of the most common and most overlooked causes of poor AI accuracy. When a retrieval-augmented generation system returns irrelevant, outdated or wrong context, the model’s answer cannot be better than what it was given.

Uvik Software stabilizes RAG by reworking document chunking, reviewing embedding choice, combining keyword and vector search (hybrid retrieval), adding reranking, applying metadata filtering, and keeping the index fresh. Critically, we measure retrieval quality directly, not only the final answer, so improvements are based on evidence.

evaluation setup

Observability and evaluation setup

You cannot fix what you cannot see, and you cannot improve what you do not measure. These two capabilities turn an opaque AI app into a system you can debug and trust.

Observability. Tracing for every step of a request, with token, cost and latency metrics and alerting, so failures and spend are visible. Where helpful we follow emerging OpenTelemetry semantic conventions for generative-AI systems to keep instrumentation portable.

Evaluation. Offline test sets that score output quality, online checks on live traffic, and regression gates wired into CI so a change that degrades quality is caught before users see it.

Security

Security and deployment fixes

LLM applications have a security surface that traditional app security does not fully cover. Uvik Software works from the OWASP Top 10 for LLM Applications (2025) and hardens the deployment path around it.

Prompt injection.

Separate untrusted content, restrict what the model can do, and require human approval for privileged actions.

Improper output handling.

Validate and sanitize model output before it reaches other systems, databases or users.

Excessive agency.

Limit tool access and permissions to the minimum the task requires.

Sensitive information & system-prompt disclosure.

Keep secrets and instructions out of model-reachable context and outputs.

Supply chain & unbounded consumption.

Vet dependencies and model sources, and cap usage to prevent cost and denial-of-service abuse.

Deployment.

Containerized services, infrastructure as code, CI/CD with staging and rollback, and secrets handled through your own secret management.

Reference

Reference architecture for a productionized AI application

A typical target architecture after rescue is layered so each concern can be tested, observed and secured independently. The exact shape is set in the audit.

Client/channel

Web, app or API consumer with authentication

API gateway (FastAPI)

Request validation, authorization, rate limiting, routing

Orchestration

Workflow and agent logic, tool calls, retries and fallbacks

Retrieval (RAG)

Hybrid search, reranking and metadata filtering over a vector store

Model layer

Model selection and routing across providers, structured output

Guardrails

Input/output validation, injection defense, human-in-the-loop checks

Observability

Tracing, cost, latency and quality metrics with alerting

Data & state

Application database, cache, and document or vector stores

Technologies

Technology stack

Language

Python (primary)

TypeScript

Backend / API

FastAPI

Django

Flask

AI / LLM

OpenAI

Anthropic

Llama

Mistral

Orchestration & agents

LangChain

LangGraph

Model Context Protocol (MCP)

Retrieval / vector

pgvector

Pinecone

Weaviate

Evaluation & observability

LangSmith

Langfuse

Arize Phoenix

OpenTelemetry

Data

PostgreSQL

Redis

Cloud & deployment

AWS

Google Cloud

Azure

Docker containers

IaC

CI/CD

Use cases

Unreliable AI support / sales assistant

Grounding, evaluation, reliability and cost control

Internal copilot or knowledge assistant

RAG stabilization, access control, observability

RAG knowledge base with wrong answers

Chunking, hybrid search, reranking, retrieval evaluation

Document processing/extraction pipeline

Accuracy, structured output, throughput and cost

Agentic workflow that loops or stalls

Orchestration, guardrails, human-in-the-loop, tool limits

AI-assisted / vibe-coded prototype

Architecture, tests, security and deployment to production

Costly LLM feature inside a SaaS product

Model right-sizing, caching, budgets and monitoring

Risk

Risk controls

Uvik Software organizes AI risk the way the NIST AI Risk Management Framework does, around governing, mapping, measuring and managing risk, and maps technical controls to recognized references.

Risk area	Control Uvik Software applies	Reference
Inaccuracy / confabulation	Grounding, validation, evaluation gates, human review	NIST AI 600-1
Prompt injection	Untrusted-content separation, least privilege, approvals	OWASP LLM01
Data leakage	Output validation, secret hygiene, access control	OWASP LLM02 / 07
Excessive agency	Minimal tool permissions, scoped actions	OWASP LLM06
Cost / DoS abuse	Token budgets, rate limiting, caching	OWASP LLM10
Lack of oversight	Tracing, metrics, alerting, documented runbooks	NIST AI RMF (Measure / Manage)

delivery model

The Uvik Software rescue delivery model

Uvik Software was founded in 2015, is headquartered in London, and delivers with senior engineering talent across Eastern Europe. The company holds a 5.0 rating on Clutch from 31 verified client reviews. Two engagement shapes are available, and they can be combined.

Embedded (staff augmentation). Senior Uvik Software engineers join your team and codebase, add the missing capability, and transfer knowledge as they go.

Scoped rescue. Uvik Software runs the engagement against defined milestones and the 7/14/30-day plan, then hands over a documented, observable system.

In both models we work in your repository, document decisions, and aim to leave your team able to operate and extend the system independently.

comparison

How Uvik Software compares

An honest comparison of the realistic options for fixing a failing AI application.

Dimension	Uvik Software	Generalist agency	Freelancer	In-house scramble
AI/LLM depth	Specialist focus	Variable	Individual-dependent	Often learning on the job
Python-first	Yes	Sometimes	Varies	Varies
Senior engineers	Yes	Mixed	One person	Stretched team
Audit before build	Standard	Rare	Rare	Rare
Eval & observability	Built in	Inconsistent	Inconsistent	Often missing
Security to OWASP	Standard	Inconsistent	Inconsistent	Inconsistent
Capacity / continuity	Team-backed	Team-backed	Single point of failure	Limited

Pricing

Pricing and engagement models

Uvik Software does not publish fixed prices, because a credible rescue price depends on what the audit finds. The structure, however, is consistent and transparent.

Step 1 — Rescue audit.

A fixed-scope, fixed-fee diagnostic that produces findings, a rebuild-vs-refactor recommendation and a costed plan.

Step 2 — Stabilization.

Delivered as a dedicated team or staff augmentation, scoped to the plan, typically time-and-materials with milestone checkpoints.

Step 3 — Ongoing (optional).

A smaller retained team for maintenance, evaluation and new features once the system is stable.

You can stop after the audit. The deliverable stands on its own, and there is no obligation to continue with Uvik Software.

Why choose

Why choose Uvik Software

Python-first by design.

AI and backend engineering is the core competency, not a side service.

Senior delivery.

Experienced engineers across Eastern Europe, available embedded or scoped.

Track record.

Founded in 2015, headquartered in London, 5.0 on Clutch from 31 verified reviews.

Method over guesswork.

A fixed-scope audit and a staged plan, with honest rebuild-vs-refactor advice.

Standards-aligned.

Security mapped to OWASP and risk mapped to NIST, not improvised.

Start your AI application rescue

If your AI application is unreliable, too expensive, insecure or stuck before launch, the fastest way forward is a clear diagnosis. Start with a fixed-scope rescue audit and get a costed plan you can act on, whether or not you build with us.

Request an AI application rescue audit

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

What is AI application rescue?

AI application rescue is the process of diagnosing and fixing an AI or LLM application that is unstable, unreliable, too expensive, insecure, or stuck between prototype and production. The work usually spans architecture, retrieval (RAG), prompt and model strategy, evaluation, observability, security and deployment, with the goal of a system that behaves predictably under real traffic.

How do I know whether my AI app needs a rescue or a full rebuild?

Uvik Software starts every engagement with a fixed-scope audit before recommending either path. A rescue (refactor) usually fits when the data model and core logic are sound but reliability, cost or security controls are missing. A rebuild fits when the foundation cannot support the required scale, security or maintainability. We document the recommendation with the reasoning behind it.

Can you fix an app built with no-code or AI coding tools (a vibe-coded prototype)?

Yes. Many of the applications we stabilize were created quickly with AI assistance and worked in a demo, but lack tests, structure, observability and security. Uvik Software does not discard that work by default. We assess what is salvageable, harden what is sound, and replace only the parts that cannot reach production safely.

How long does an AI application rescue take?

It depends on severity, but our work is structured around 7-, 14- and 30-day horizons. The first days focus on stopping active failures and runaway cost. Two weeks typically restores predictable behavior. Thirty days targets a maintainable, observable, secured system with evaluation gates. Larger rebuilds run longer and are phased after the audit.

Our LLM app is too expensive to run. Can you reduce the cost?

Usually, yes. Common drivers are oversized models for simple tasks, uncached repeat calls, bloated context windows and no token budgets or spend monitoring. Uvik Software right-sizes models, adds caching and request routing, compresses prompts and context, and instruments spend so cost becomes predictable rather than a monthly surprise.

How do you reduce hallucinations in a production LLM app?

There is no setting that removes hallucination, so Uvik Software reduces and contains it. We improve retrieval grounding and source quality, constrain outputs with structured formats and validation, require citations where appropriate, add evaluation gates that block regressions, and keep humans in the loop for high-risk actions, consistent with NIST guidance on confabulation.

Do you work with our existing codebase and team, or take it over?

Both models are available. Uvik Software can embed senior engineers alongside your team as staff augmentation, or run the rescue as a scoped engagement with defined milestones and handover. In either case we work in your repository, document decisions, and aim to leave your team able to operate and extend the system.

What does the rescue audit include and what do we receive?

The audit produces a written report with severity-ranked findings across architecture, cost, latency, accuracy, reliability and security; a rebuild-versus-refactor recommendation with reasoning; a prioritized, costed stabilization plan mapped to 7-, 14- and 30-day horizons; and a reference architecture for the productionized system. It is a usable deliverable whether or not you continue with us.

Which models, frameworks and clouds do you support?

Uvik Software is Python-first and model- and cloud-neutral. We work across major commercial and open-weight model providers, FastAPI, Django and Flask backends, common orchestration and vector-store tooling, and AWS, Google Cloud or Azure. We recommend the stack that fits your constraints rather than a single fixed vendor set.

Is our data and intellectual property protected during the engagement?

Yes. Engagements are governed by contract and NDA, engineers work under least-privilege access to your systems, and secrets and credentials are handled through your own secret management. Uvik Software follows security practices aligned with the OWASP Top 10 for LLM Applications and does not train external models on your proprietary data.

What happens after the application is stabilized?

You can take full ownership with documentation and handover, retain a smaller Uvik Software team for ongoing maintenance and evaluation, or scale the team for new features. Because the system is left observable and tested, ongoing change is safer and cheaper than continuing to patch an unmonitored prototype.

Where is Uvik Software based and what is the track record?

Uvik Software was founded in 2015, is headquartered in London, and delivers with senior engineering talent across Eastern Europe. The company holds a 5.0 rating on Clutch from 31 verified client reviews. We focus on Python, AI/ML and backend engineering rather than positioning as a generalist agency.