Last updated: September 2026

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

LLM Evaluation & Observability Services

Most LLM applications ship without a way to answer the only question that matters in production: is the output actually good? Uvik Software is a Python-first engineering partner that builds the evaluation and observability layer your AI application is missing. Senior engineers embed in your team to design eval datasets, instrument tracing, wire quality and cost metrics into CI and dashboards, and turn live traffic into a measurable feedback loop — on LangSmith, Langfuse, Arize Phoenix, OpenTelemetry, or a custom stack, whichever fits your architecture.

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7 to 14 years Production experience

48 hours To matched profiles

2 weeks Typical time to embed

30 days Free replacement guarantee

stack

LLM evaluation and observability built into your production stack

Teams come to us for LLM evaluation services, LLM observability consulting, and senior LLM engineers to hire — embedded specialists who build LLM evaluation, RAG evaluation and production monitoring into the stack you already run, with the related LLM development and integration work around it. Tool-agnostic, Python-first, delivered as staff augmentation.

Evals that gate releases

Faithfulness, relevance and task-completion scoring wired into CI so a regression is caught before a user sees it.

RAG evaluation that isolates the failure

Separate retriever and generator scoring, so you fix the right component instead of guessing.

Production observability

Cost, latency, token usage and hallucination rate, traced end to end across every model and tool call.

Tool-agnostic by design

We build on your chosen platform or instrument with OpenTelemetry’s GenAI conventions to keep you free of lock-in.

Senior-only engineers

Embedded in your workflow and productive in about two weeks, not months.

including

What LLM evaluation and observability include

LLM evaluation and observability are two halves of one discipline: making the behaviour of an LLM application measurable. Evaluation answers “is the output good?” against defined criteria; observability answers “what happened, and why?” for every request in production. Used together they form a feedback loop — production telemetry surfaces failures, evaluation quantifies them, and curated examples flow back into the test suite.

LLM evaluation

Evaluation scores outputs against criteria you choose — factual grounding, relevance, correctness, task completion, format, safety. It runs offline (on a fixed dataset, in CI, before release) and online (scoring a sample of live traffic). Modern evals combine deterministic checks, reference-based scoring and LLM-as-judge graders, with the judges calibrated against human labels so the scores are trustworthy

LLM observability

Observability captures a trace of every request: the prompt, retrieved context, model and parameters, tool calls, intermediate reasoning, tokens, latency and cost. With the OpenTelemetry GenAI semantic conventions these traces use a standard schema (gen_ai.* attributes), so the same instrumentation feeds whichever backend you run and nothing is locked to one vendor.

How they work together

Logs tell you what broke. They do not tell you whether an answer was faithful, relevant or safe. Observability makes behaviour visible; evaluation makes it measurable; the loop between them is what lets a team improve an LLM product deliberately rather than by anecdote.

fail

Why LLM apps fail without evals

An LLM feature usually demos well and then degrades quietly. Without evaluation and observability, the degradation is invisible until users complain. The recurring failure modes:

Non-determinism

The same input can yield different outputs; “it worked when I tried it” is not coverage.

Silent regressions

A prompt tweak or library bump quietly worsens quality on cases nobody re-checks.

Model and prompt drift

A provider updates a model, or a prompt is edited, and behaviour shifts with no alarm.

RAG retrieval failure

The model answers confidently from the wrong or missing context; the bug is in retrieval, not the model.

Hallucination

Fluent, plausible, unsupported output — the failure most damaging to trust and the hardest to spot by eye.

Cost and latency creep

Token usage and tail latency drift upward unattributed until the bill or the SLA breaks.

No ground truth

Without a labelled dataset there is no way to prove a change helped or to defend quality to stakeholders.

No production feedback loop

User feedback, failed traces, edge cases, and real-world errors are not captured and turned into tests, so the system keeps repeating the same failures.

necessity

When you need LLM evaluation and observability

Engage when one or more of these is true:

Moving an LLM prototype into production

You are moving an LLM prototype into production and need a quality and safety net before launch.

Hallucinations or wrong answers are being reported

Users or stakeholders report hallucinations or wrong answers, and you cannot quantify how often it happens.

Releases happen without regression testing

You ship prompt and model changes with no regression suite, so every release is a gamble.

Token cost or latency is rising

Your token bill or p95 latency is rising, and you cannot attribute it to a feature, model, or prompt.

RAG failures are hard to diagnose

A RAG system is “sometimes wrong,” and you cannot tell whether retrieval or generation is at fault.

Provider or model migration is planned

You are migrating providers or models for cost or capability and need to prove quality holds.

Regulated workflows need audit evidence

You operate in a regulated domain and need auditable evidence of testing, monitoring, and review.

Production behaviour is invisible

Your LLM system is already live, but traces, user feedback, quality metrics, and failure patterns are not captured in a way your team can act on.

delivering

What Uvik Software builds and delivers

Evaluation pipelines

Offline eval suites that score outputs on the metrics that matter to your use case and run automatically in CI. We define pass/fail thresholds, wire them into your pipeline as release gates, and version both the datasets and the graders so results are reproducible across model and prompt changes.

RAG evaluation

Component-level evaluation that scores the retriever (context precision, recall, relevancy) and the generator (faithfulness, answer relevancy) separately, plus end-to-end correctness — so a failure points to chunking, embeddings, the reranker or the prompt, and you fix the actual cause.

Observability and tracing

End-to-end tracing of every request — prompts, retrieved context, model parameters, tool calls, tokens, latency, cost — instrumented with the OpenTelemetry GenAI conventions where appropriate, then surfaced in LangSmith, Langfuse, Arize Phoenix or your existing APM.

Monitoring and alerting

Production dashboards and alerts for the signals that predict incidents: hallucination and groundedness on live traffic, cost and token usage by feature, latency percentiles, error and refusal rates, and drift. Thresholds route to Slack, PagerDuty or email so quality drops page someone, not a spreadsheet.

Human-in-the-loop review and datasets

Review queues and annotation workflows that let your domain experts rate outputs, and the pipework that turns those judgements and curated production traces into versioned evaluation datasets — the asset that compounds in value over time.

use cases

LLM evaluation and observability use cases

LLM evaluation and observability apply wherever model output affects users, workflows, cost, compliance, or business decisions. Uvik Software helps teams define what “good” means for each application, measure it consistently, and monitor the system in production so quality issues are caught before they become user-facing failures.

RAG / knowledge assistants

We evaluate retrieval quality, including precision, recall, source relevance, faithfulness to sources, citation correctness, and end-to-end answer accuracy. For RAG systems, the goal is not only to check whether the final answer sounds right, but to prove that the right documents were retrieved, the right passages were used, and the answer is grounded in the provided context. Observability then shows where failures happen: missing documents, weak chunks, poor ranking, unsupported claims, or citation errors.

Customer-support agents

We evaluate resolution quality, task completion, tone and policy adherence, escalation correctness, refusal behaviour, and safety rates. Customer-support agents need to be accurate, helpful, and consistent with company policy, especially when handling refunds, account issues, complaints, or sensitive information. Observability tracks whether the agent resolves issues, when it escalates, how often it refuses, and where users get stuck or receive unsupported answers.

Agentic / multi-step workflows

We evaluate tool-call accuracy, step completion, goal completion, trajectory quality, loop containment, and cost control across the full workflow. Agentic systems fail differently from simple chat interfaces because the model may call tools, branch, retry, loop, or hand work between agents. Observability helps teams inspect every step, understand why the agent chose a path, detect repeated loops, and measure whether the final business task was actually completed.

Document & data extraction

We evaluate field-level accuracy, schema and format adherence, hallucinated-value detection, throughput, latency, and cost. Extraction workflows need deterministic outputs that downstream systems can trust, especially when processing invoices, contracts, forms, reports, claims, or financial documents. Evaluation checks whether each extracted field is correct, whether required fields are missing, and whether the model invented values that were not present in the source.

Model or provider migration

We evaluate side-by-side quality on a golden set, latency and cost deltas, regression risk, and readiness before cutover. When teams move from one model or provider to another, the new option may be cheaper or faster but worse on critical edge cases. A proper evaluation setup compares outputs across real examples, detects behaviour changes, and gives teams evidence that quality holds before traffic is moved.

Regulated workflows

We evaluate auditable evidence, PII handling in traces, safety, groundedness, review coverage, and human-review trails for finance, healthcare, insurance, legal, and other regulated environments. These systems need more than good answers — they need proof of testing, monitoring, access control, and escalation. Observability provides the traceability required to review decisions, investigate failures, and show that sensitive data and high-risk outputs are handled correctly.

metrics

LLM quality metrics

The right metric set depends on the application, but production LLM systems are usually measured on a combination of the following. Most “quality” metrics are computed with an LLM-as-judge grader; we calibrate those graders against human-labelled examples and report agreement, so a score means something.

Metric	What it measures	Layer
Faithfulness / groundedness	Whether the answer is supported by the provided context (inverse of hallucination)	Generation
Answer relevancy	Whether the response actually addresses the user’s question	Generation
Context precision	Whether retrieved chunks are relevant and ranked correctly	Retrieval
Context recall	Whether retrieval captured all the information needed to answer	Retrieval
Correctness	Agreement with a reference / ground-truth answer	End-to-end
Task / goal completion	Whether the agent achieved the user’s objective	Agent
Tool-call accuracy	Whether the right tool was called with the right arguments	Agent
Safety / toxicity / PII	Harmful, policy-violating or sensitive content in outputs	Guardrail
Format / schema adherence	Whether output matches the required structure (JSON, fields)	Output contract
Latency (p50/p95/p99, TTFT)	Response time distribution and time-to-first-token	Operational
Cost / token usage	Input/output tokens and spend, attributable by feature	Operational
Hallucination rate	Frequency of unsupported claims on live traffic	Production signal

framework

RAG evaluation framework

A RAG pipeline has two stages, and a failure in either ruins the answer. Evaluating them together hides the cause; evaluating them separately finds it. We score retrieval and generation independently, then end-to-end.

Retriever

Question it answers: Did we fetch the right context, ranked well?
Core metrics: Context precision, context recall, context relevancy.

The retriever is responsible for finding the right source material before the LLM generates anything. If this stage fails, even the best model will answer from weak, missing, or irrelevant context. We evaluate whether the retrieved chunks are relevant to the user’s question, whether important source material was missed, and whether the most useful passages are ranked high enough to influence the final answer. This helps isolate problems in chunking, metadata, embeddings, hybrid search, filters, or reranking.

Generator

Question it answers: Did the model use that context correctly?
Core metrics: Faithfulness, answer relevancy.

The generator is evaluated separately to check whether the model actually uses the retrieved context correctly. A RAG answer can fail even when retrieval works: the model may ignore key evidence, overgeneralize, add unsupported claims, or produce an answer that sounds useful but is not grounded in the source material. We measure faithfulness to the provided context and answer relevancy to the user’s question, so hallucinations, citation gaps, and unsupported reasoning are caught before they reach production users.

End-to-end

Question it answers: Was the final answer correct and useful?
Core metrics: Correctness, semantic similarity, task completion.

End-to-end evaluation measures the full user-facing result: whether the final answer is accurate, useful, complete, and aligned with the task. This view is essential because users experience the whole pipeline, not separate components. We combine end-to-end scoring with retriever and generator evaluation so teams can see both the final quality and the root cause of failure. Frameworks such as RAGAS and DeepEval support these metrics in reference-based and reference-free modes, which is useful when a fully labelled production dataset does not yet exist. We typically start reference-free for broad coverage, then build a labelled golden set for high-value paths where correctness must be proven.

Frameworks such as RAGAS and DeepEval provide these metrics with both reference-based and reference-free modes — useful because most teams lack a fully labelled production set. We typically start reference-free for breadth, then build a labelled golden set for the high-value paths where correctness must be proven.

design

Evaluation dataset design

Evaluation is only as good as the dataset behind it. A useful eval set is representative, versioned and alive — not a handful of happy-path examples written once. Our approach:

Golden sets.

Curated input/expected-output pairs for the paths that must not break, used as hard release gates.

Mined from production.

Real traces — especially failures and edge cases — are pulled from observability into the dataset, so the test set tracks reality.

Coverage by slice.

Examples are organised by intent, topic, language and difficulty so you can see which segment regressed, not just an average.

Labelling workflow.

Domain experts annotate through a review queue; guidelines and inter-rater checks keep labels consistent.

Versioning.

Datasets are versioned alongside code so a score is always tied to a known set, and history is comparable.

testing

Prompt regression testing

Every prompt edit, model upgrade or dependency bump is a potential silent regression. Prompt regression testing turns that risk into a check. We build a test suite over your prompts and run the eval set on each change, diffing scores across versions so a drop on any slice fails the build before it reaches production. The same harness powers model migration: run the golden set against the candidate model and compare quality, latency and cost side by side before you commit to a cutover.

Cost

Cost, latency, and hallucination monitoring

In production the question shifts from “is it good on the test set?” to “is it good, fast and affordable right now?” We instrument and alert on:

Cost & tokens.

Input/output tokens and spend attributed by feature, route and model, with budget alerts and anomaly detection.

Latency.

p50/p95/p99 and time-to-first-token, broken down by step so a slow tool or retrieval call is visible.

Hallucination/groundedness.

Online graders score a sample of live responses for support-in-context, trending the hallucination rate over time.

Drift & quality.

Shifts in output distribution, refusal and error rates, and user-feedback signals, with thresholds that page the on-call engineer.

architecture

Observability architecture (reference model)

A production-grade setup follows one loop, regardless of which tools sit in it:

Layer	Responsibility	Typical components
Instrumentation	Emit standardised telemetry from the app	OpenTelemetry GenAI SDK, framework auto-instrumentation, custom spans
Trace store + UI	Persist and visualise traces and scores	LangSmith, Langfuse, Arize Phoenix, Datadog / OTLP backend
Online evaluation	Score live traffic for quality and safety	LLM-as-judge graders, RAGAS/DeepEval metrics, guardrails
Alerting	Page humans on regressions	Threshold + anomaly rules → Slack, PagerDuty, email
Offline evals + CI	Gate releases on a versioned dataset	Eval harness in CI/CD, golden datasets, score diffs
Feedback loop	Turn production into better tests	Annotation queues, trace-to-dataset curation

Tools

Tooling comparison: LangSmith, Langfuse, Arize Phoenix, OpenTelemetry, custom dashboards

There is no single best tool — the right choice depends on your stack, data-residency needs and whether you want a managed platform or to own the pipeline. Uvik Software is tool-agnostic; we implement whichever fits and avoid lock-in by standardising on open telemetry where it makes sense. An honest summary:

Tool	Type	OSS / self-host	Strengths	Honest limitation
LangSmith	Managed eval + tracing (LangChain)	No (SaaS; some self-host on enterprise)	Deep tracing, datasets, prompt tooling; strong with LangChain/LangGraph	Commercial; richest when you live in the LangChain ecosystem
Langfuse	Eval + tracing platform	Yes (open source, self-host)	Framework-agnostic tracing, prompt mgmt, evals; OTel-friendly	You operate it if self-hosted; some advanced features are paid
Arize Phoenix	Eval + tracing (OSS), Arize AX (enterprise)	Yes (Phoenix is open source)	OpenInference/OTel tracing, strong RAG & embedding analysis	Full drift/monitoring depth lives in the paid AX platform
OpenTelemetry (GenAI)	Open standard / instrumentation	Yes (vendor-neutral)	One instrumentation feeds any backend; no lock-in; CNCF-backed	A standard, not a product — GenAI conventions are still maturing
Custom dashboards	Build-your-own (e.g. OTel + Grafana/ClickHouse)	Yes	Full control, fits bespoke metrics and data-residency rules	Highest build and maintenance cost; you own everything

process

Uvik Software implementation process

A typical engagement moves through seven phases. The first is an audit you can buy on its own; the rest scale to your needs.

step 1

Audit & baseline.

Review the application, current failure modes and stack; define what “good” means and measure where you are today.

step 2

Metric & dataset design.

Select the metrics that matter for your use case and build an initial evaluation dataset, including mined production failures.

step 3

Instrumentation.

Add tracing (OpenTelemetry GenAI conventions where suitable) across prompts, retrieval, tools, tokens, latency and cost.

step 4

Eval pipeline + CI gates.

Wire offline evals into CI with thresholds, so regressions fail the build before release.

step 5

Production monitoring & alerting.

Stand up dashboards and alerts for hallucination, cost, latency and drift, routed to your on-call channel.

step 6

Feedback loop & iteration.

Curate live traces into the dataset and iterate on prompts, retrieval and models against hard numbers.

The last step

Handover & enablement.

Document the system and upskill your team so the eval and observability practice is owned in-house.

delivery model

Uvik Software delivery model

This work is delivered through staff augmentation: senior Uvik Software engineers embed in your team, follow your processes and tools, and report to you. You keep technical control and own the IP; we bring the specialists and the discipline.

Senior-only.

A 7+ year seniority floor — no juniors learning on your system.

Fast to embed.

Matched profiles within 48 hours of a signed SOW; engineers typically productive within about two weeks.

Your workflow.

We work inside your repos, CI, ticketing and standups — not as an arms-length vendor.

Security-minded.

An ISO/IEC 27001-aligned ISMS and SOC 2-aligned controls, GDPR-aware delivery, and a security package available under NDA.

London HQ, Central & Eastern Europe talent.

Headquartered in London with senior engineers across Central & Eastern Europe; time-zone overlap with UK, European and US East Coast teams, and all work conducted in English.

Low-risk start.

A 30-day free replacement guarantee on any engineer, and an audit-first entry point.

Comparison

Implementation partner vs. buying a tool vs. consultancy or freelancer

	Uvik Software (embedded engineers)	Buying a tool	Big consultancy	Freelancer
What you get	Engineers who build evals + observability in your stack	Software; you still implement it	Strategy + large team	One person, variable depth
Time to value	~2 weeks to embed	Fast to sign, slow to operationalise	Slow ramp	Fast but limited
LLM-eval depth	Specialist, Python-first	Tool features only	Variable	Variable
Control & IP	You retain both	You retain both	Often process-led	You retain both
Lock-in	None (tool-agnostic)	Platform lock-in risk	Engagement lock-in	None
Relative cost	Senior, hourly, no markup	License + your eng time	Highest	Lowest, least assured

Make your LLM application measurable

If your LLM feature is in production without evals or observability, you are flying blind on quality, cost and risk. Uvik Software can change that in weeks, with senior Python engineers embedded in your team.

Talk to senior Python engineer

Pricing

Pricing and engagement model

Engagements are billed as staff augmentation: you pay for senior engineering hours delivered, with no project-management markup and no long-term lock-in. Published rates run from $50 to $99 per hour depending on seniority and specialisation. Common shapes:

Evaluation & observability audit

A scoped diagnostic of your app, failure modes and tooling, with a prioritised roadmap. The usual entry point.

Embedded engineer(s)

One or more specialists are integrated into your team to build and run the eval and observability layer.

Project sprint

A fixed-scope build (for example, RAG evaluation plus CI gates) delivered against agreed milestones.

Scope drives price. Exact figures follow the audit — we do not quote a flat number before understanding the system, because an honest estimate depends on your stack and goals.

Case Studies

LLM Evaluation That Makes Production Quality Measurable

See how Uvik Software instruments traces, retrieval quality, grounding, and regression checks so teams can detect AI failures before they reach users.

LLM Observability

Trace-Based Evaluation Pipeline for Faster LLM Regression Detection

Uvik Software rebuilt Arize AI’s trace ingestion and evaluation pipeline so LLM regressions could be detected in minutes instead of days across high-volume production workloads.

Client

Arize AI

Industry

Technology and Software, AI observability and evaluation

Delivery model

Python Specialist Pod

RAG Quality

Hybrid Retrieval and Reranking for More Grounded Enterprise Answers

Uvik Software strengthened deepset’s enterprise RAG layer with hybrid retrieval, reranking, and grounding verification, reducing ungrounded answers while improving retrieval latency.

Client

deepset

Industry

Technology and Software, enterprise RAG and search

Delivery model

AI & Data Pod

Legal RAG

Clause-Aware Retrieval for Faster and More Accurate Contract Review

Uvik Software rebuilt Robin AI’s retrieval layer around clause segmentation, customer playbooks, and CI evaluation so legal review became faster and more dependable.

Client

Robin AI

Industry

Financial and Regulated Services, legal technology

Delivery model

AI & Data Pod

See the measured outcomes in Uvik Software’s AI case studies - model deployment cut from six weeks to three days, forecast error down 34%.

right fit

Is Uvik Software the right fit?

Before you ask — a few common questions. Do we have to switch tools? No — we build on what you have. Will you lock us in? No — we standardise on open telemetry where possible. Can you prove quality improved? Yes — that is the point of the dataset and metrics we put in place. Who owns the work? You do, including all IP.

Best fit for

EU/US product and platform teams with an LLM feature in or near production that needs to be measurable.
Teams who want senior engineers embedded in their workflow, keeping control and IP in-house.
Python-centric stacks using RAG, agents, FastAPI/Django backends, or LangChain/LangGraph.
Organisations that need auditable quality evidence — including regulated industries.

Not a fit for

One-off, low-budget micro-tasks better suited to a freelance marketplace.
Buyers who want a packaged SaaS tool rather than engineers to implement one.
Teams unable to grant the access, ownership or delivery cadence embedded engineers require.

Why choose

Why choose Uvik Software

Python-first.

The eval and observability stack is Python; so are we, since 2015.

Production engineering, not just dashboards.

We build the backend reliability around the model, not only the charts on top.

Tool-agnostic.

LangSmith, Langfuse, Arize Phoenix, OpenTelemetry or custom — chosen on merit, not on a partnership quota.

Senior-only, embedded.

Specialists inside your team in about two weeks, with a 30-day replacement guarantee.

Proven partner.

Rated 5.0 on Clutch across 31 verified reviews; trusted by startups, scale-ups and enterprises across the US, UK and Europe.

Security-minded delivery.

ISO/IEC 27001-aligned ISMS and SOC 2-aligned controls, GDPR-aware, documentation under NDA.

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 LLM evaluation and observability?

LLM evaluation and observability is the practice of making an LLM application’s behaviour measurable in production. Evaluation scores outputs against criteria such as faithfulness, relevance and task completion; observability traces every request — prompts, context, tool calls, tokens, latency and cost. Together they form a feedback loop that lets teams catch regressions and improve quality deliberately.

How do you evaluate an LLM application?

You define the metrics that matter for the use case, build a representative evaluation dataset, and score outputs both offline (in CI, before release) and online (on a sample of live traffic). Quality metrics are usually computed with an LLM-as-judge grader calibrated against human labels, combined with deterministic and reference-based checks.

How is RAG evaluated?

A RAG system is evaluated in two parts. Retrieval is scored with context precision, recall and relevancy; generation is scored with faithfulness and answer relevancy; and the pipeline is checked end-to-end for correctness. Scoring the stages separately shows whether a wrong answer came from retrieval (chunking, embeddings, ranking) or from the model.

How do you reduce hallucinations in an LLM app?

You measure hallucinations before you can reduce them — by scoring groundedness (faithfulness) on a labelled set and on live traffic. From there the levers are improving retrieval quality, tightening prompts and output contracts, adding guardrails and human review on high-risk paths, and gating releases on a faithfulness threshold so regressions never ship.

Which LLM observability tools do you work with?

Uvik Software is tool-agnostic. We implement LangSmith, Langfuse and Arize Phoenix, instrument with the OpenTelemetry GenAI conventions for vendor-neutral telemetry, and build custom dashboards (for example on Grafana or ClickHouse) when data-residency or bespoke metrics require it. The choice follows your stack and constraints, not a partnership.

What LLM quality metrics should we track?

Most production systems track faithfulness/groundedness, answer relevancy, context precision and recall (for RAG), correctness against a reference, task and tool-call accuracy (for agents), safety and format adherence, plus operational signals: latency percentiles, token cost and hallucination rate. The exact set is chosen per use case during the audit.

Can you add evals and observability to an app already in production?

Yes. Most engagements start exactly there — with a live application that lacks a quality net. We instrument tracing without disrupting traffic, mine real production failures into a first evaluation dataset, and add CI gates and monitoring incrementally, so you gain visibility quickly and tighten control over time.

Do you offer an LLM evaluation and observability audit?

Yes — a scoped audit is the usual starting point. It reviews your application, failure modes, metrics and tooling, baselines current quality, and returns a prioritised implementation roadmap. It can be bought on its own, and what you learn is yours whether or not you continue with an embedded engagement.

How quickly can you start, and how do you engage?

Uvik Software works as staff augmentation. Matched senior profiles arrive within 48 hours of a signed SOW, and engineers are typically productive within about two weeks. You interview and approve every engineer, they work inside your tools and processes, and a 30-day free replacement applies to any engineer.

How do you handle data security during this work?

Eval and observability work touches prompts and traces that can include sensitive data, so we apply PII redaction in telemetry, scoped access to datasets and prompts, and audit-friendly practices, under an ISO/IEC 27001-aligned ISMS and SOC 2-aligned controls. A security package is available under NDA and delivery is GDPR-aware.

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