Teams Stuck at the Proof of Concept
The demo worked, but nobody trusts it with real users or real data. We add the evaluation, access control, monitoring and fallbacks a production system needs.
Stop Demoing AI.
Put It to Work.
LLM integrations, RAG copilots and AI agents that use your tools, built with access control, evaluation and cost limits from day one.
Trusted across Europe
Industries we serve.
Engineering teams in regulated, mission-critical industries — every engagement audited, documented, and production-grade.
Banking & Payments
FinTech
PCI-DSS compliant payments and core banking infrastructure — sub-100ms p99 latency, end-to-end audit trail, and tokenization at the edge.
PCI-DSS · ISO 27001
Patient Data
Healthcare
HIPAA-aware patient data pipelines
HIPAA · SOC2
5G & Networks
Telecom
5G core network observability at scale
NFV · ETSI MANO
Retail & Marketplaces
E-Commerce
99.99% uptime during peak traffic events
PCI-DSS · GDPR
Sovereign & Public
Government
Sovereign cloud with full audit trails
eIDAS · FIPS 140-2
Fleet & IoT
Logistics
Real-time fleet tracking & IoT ingestion
MQTT · OPC-UA
15+Years in business (since 2011)
3Own AI products in production
24/7Monitoring and support
EUData residency option
What we deliver
Our AI agent and LLM services
From the first model call to agents your teams rely on every day
Put the right model behind each use case — OpenAI, Anthropic Claude or open-weight models such as Llama, Mistral and Qwen. We build the integration layer with structured outputs, retries and fallback between providers and, when data cannot leave your perimeter, private deployment in your cloud or on your own servers.
OpenAI · Anthropic · open-weight · private deploymentAnswers grounded in your documents, wikis and tickets, with citations to the source. Retrieval is filtered by the permissions people already have in the source systems, so a copilot answers from the documents each user is allowed to open.
Cited answers · per-document access controlAgents that do work, not just chat: they call your APIs through function calling and MCP servers, read and update records, and hand off to a person. Sensitive actions — payments, deletions, outbound messages — wait for human approval, and every tool call is logged.
Function calling · MCP · human approvalReplace the copy-paste between inboxes, spreadsheets and back-office systems. We put LLM steps inside your existing workflows — triage, routing, drafting, enrichment — with clear rules for what runs on its own and what goes to a reviewer.
Triage · routing · drafting · review queuesExtract, classify and validate data from invoices, contracts, forms and reports — scans included. The output lands in your systems as structured fields, with confidence levels and a human review queue for the cases the model is unsure about.
Extraction · classification · structured outputKnow whether a prompt or model change makes things better before it ships. We build evaluation sets from your real cases, trace every request end to end, and set per-feature token budgets, caching and model routing so the bill stays predictable.
Evaluation sets · tracing · token budgetsFree assessment
Get a free AI agent assessment
Our engineers review your current setup and deliver a prioritized roadmap — no strings attached.
Who we help
Teams moving AI past the pilot
The four situations where this engagement usually pays back fastest.
Companies with Knowledge Spread Across Tools
Answers live in Confluence, SharePoint, tickets and shared drives, and people spend their day asking colleagues. We build a copilot that searches all of it and respects who can see what.
Operations Teams Buried in Repetitive Work
Emails to triage, documents to key in, tickets to route. We automate the predictable part and leave the judgement calls to your people.
Organizations Whose Data Cannot Leave the Building
Healthcare, finance and public-sector teams that need AI without sending sensitive data to a third-party API. We deploy open-weight models in the cloud region you choose or on your own infrastructure.
Own product
NexTalk: AI agents for live chat
01 / 02
NexTalk is Privum's live chat platform: AI agents answer website visitors at any hour using the customer's own product documentation, and hand the conversation to a person when it matters.
Tech stack
ClaudeGPTGeminiLlamaOpenRouterWebSocket
Answer visitors 24/7 with whichever LLM provider each customer chooses, keep every customer's data and API keys isolated, and know when the AI should stop and a person should take over.
Bring-your-own-key AI agents across eight providers (Claude, GPT, Gemini, Llama, DeepSeek, Mistral, Qwen and OpenRouter), human handoff, tenant isolation, AES-encrypted API keys and HMAC-signed webhooks.
In production as a SaaS product — and it is the live chat running on privum.cloud.
Outcomes & method
AI agents built for production
Most AI projects stall between the demo and the first real user. We close that gap with the same engineering discipline we apply to any production system: access control, testing, observability and a clear owner for every failure mode.
Business outcomes
- 01
Answers you can check
Grounded responses with citations, and evaluation sets built from your own cases, so quality is measured, not assumed.
- 02
Automation with guardrails
Agents act through narrowly scoped tools, and sensitive actions wait for human approval.
- 03
Predictable cost
Token budgets, caching and model routing keep spend tied to value as usage grows.
How we implement
- 01
Discover & prioritize
We map candidate use cases, the data behind them and the risks, and pick the one with the clearest payback.
- 02
Prototype on real data
A working prototype on a sample of your data, with an evaluation set agreed with your team.
- 03
Build for production
Integration with your systems, access control, guardrails, tracing and human-approval steps.
- 04
Launch & measure
A gradual rollout to real users, with quality, latency and cost tracked from the first day.
- 05
Operate & improve
We monitor, re-run the evaluations on every change and tune prompts, retrieval and models — or hand over to your team.
Engagement model
How we work
From first call to production — a proven 4-step engagement model that keeps the conversation transparent and the velocity honest.
- 01
Discovery
We audit your current stack, identify gaps, and align on business goals.
- 02
Assessment
A detailed roadmap with priorities, effort estimates, and quick wins.
- 03
Delivery
Our engineers embed with your team and execute sprint by sprint.
- 04
Support
Ongoing monitoring, optimization, and knowledge transfer to your team.
Related disciplines
Related services
Adjacent practices that pair well with this one — most engagements blend two or three.
Common questions
Frequently asked questions
Practical answers about scope, timelines, and how engagements with our AI agent team usually look.
We are model-agnostic. We work with OpenAI and Anthropic models, Google Gemini, and open-weight models such as Llama, Mistral and Qwen. We choose per use case, based on quality on your evaluation set, latency, cost and where the data is allowed to go — and we keep the integration layer provider-neutral, so you can switch models later without a rewrite.
We start by classifying the data each use case touches, then choose the deployment to match: provider APIs under enterprise terms that exclude training on your data, EU-hosted endpoints, or self-hosted models when data cannot leave your perimeter. Retrieval enforces per-document permissions, prompt logs are minimized and personal data masked, and we document the processing so your DPO can complete the records of processing and the DPIA.
No language model is infallible, so we design around it. Answers are grounded in retrieved sources and cite them, the model is allowed to say it does not know, and outputs that feed other systems are validated against a schema. Before launch we build an evaluation set from your real cases and run it on every prompt, model or retrieval change, so quality is measured rather than assumed.
There are two parts: the build, which we scope as a fixed project or a dedicated team after the initial assessment, and the running cost — model tokens, vector storage and hosting. We estimate the cost per request during the prototype, then keep it in check with caching, smaller models for simple steps, model routing and per-feature budgets with alerts.
Yes. Open-weight models can run on your own GPU servers or in a private cloud account in the EU region you choose, served behind an API that only your applications can reach. We help you size the hardware, pick the model that meets your quality bar, and set up serving and monitoring — our MLOps team covers the infrastructure side.
It depends on the use case and on your systems, so we plan in phases rather than promising a date upfront. A focused prototype on your data usually comes first; production adds integration, access control, evaluation and monitoring. After the initial assessment you get a phased plan with an estimate for each stage.
Talk to engineering
Let's talk about your AI agent strategy
Whether you're starting from scratch or scaling what you have, our engineers are ready to help.