Most companies launch an AI pilot. We run what comes after — the monitoring, the optimisation, the new builds, and the reporting that turn a first deployment into an operational AI layer.
Both paths begin with the same first step: a 30-minute discovery session.
Book a free discovery sessionNot a support contract. An ongoing technical delivery engagement in which Liorant acts as your embedded AI operations team — building, monitoring, optimising, and expanding your AI systems as your business evolves. Every engagement runs across seven active workstreams.
Track credit consumption, connector health, prompt drift, and model behaviour — continuously.
Tune prompts, retrieval, and conversation design based on real usage transcripts.
DLP review, access audit, EU AI Act mapping, and PII checks — on a monthly cycle.
One or two new agents or workflows per month, per tier, from the opportunity backlog.
Monthly impact in euros and hours; a full business review every quarter.
Office hours, prompt-library updates, and platform-change briefings for power users.
A live 90-day backlog of identified opportunities, each with estimated business value.
workstreams, always active — never a maintenance contract with an empty backlog.
Platforms change, connectors break, models shift behaviour between versions, and credit budgets drift without visibility. Liorant monitors every active system continuously — credit consumption, failed topics, token expiry, broken integrations, hallucination rates on key workflows. If something stops performing, we catch it before your team does.
A deployed agent is a starting point, not a finished product. We review real usage transcripts, identify where responses fall short, and iterate — adjusting prompts, refining retrieval logic, redesigning conversation flows. Reactive in months one to three; proactive and usage-driven from month four onward.
Every AI system that handles business data carries compliance obligations. We run a monthly governance cycle: DLP checks, access-rights audits, data-residency verification, PII-leakage review. For clients in Spain and the EU, we map each deployment against EU AI Act risk classifications and maintain documentation as obligations phase in through 2026–2027.
The most effective way to retain the value of an operations engagement is to keep expanding what it covers. Each month we build and deploy one or two new agents, flows, or Claude Projects — depending on your tier — drawing from the opportunity backlog. Major builds are scoped as change orders at defined rates.
We report in euros and hours, not uptime percentages. Every month your AI team receives a written outcome report: time saved, errors avoided, leads qualified, documents processed — connected to what it was worth. Every quarter, a full business review covers value delivered, open risks, and the next priorities. Co-authored with your internal champion.
Unused AI systems are cancelled AI systems. We run monthly open office hours for power users, maintain a refreshed prompt library, and deliver a quarterly training session when significant platform updates ship. Adoption is a leading indicator of long-term value.
We maintain a live 90-day AI opportunity backlog — jointly owned with your team — that identifies the next automation targets, estimates their business value, and sequences them by effort and impact. This backlog answers the question every decision-maker eventually asks: why do we still need you? A retainer with an empty backlog is a maintenance contract. Ours never is.
It begins with a bounded activation project — then continues as the operational layer your business runs on.
A time-limited, fixed-price engagement. Discovery, platform selection, build, test, documentation, and handoff with governance guardrails in place. Scope is fixed; delivery is concrete and verifiable. You know exactly what you're getting before you commit.
Begins after Phase 1 completes. Liorant takes ownership of your AI systems on an ongoing basis, running all seven workstreams at the tier that fits your current scale. Not sold to cold prospects — sold to clients who have already seen what we build.
Phase 2 terms are included in the Phase 1 SOW. You sign once — and never face a second sales conversation when Phase 1 ends.
Choose the tier that fits where you are now — and move up when the business case supports it.
✔ included · — not included. Specific tier pricing is disclosed in consultation.
For teams that have completed activation and need their AI systems kept healthy and reported. Suitable when the portfolio is stable and the primary need is monitoring, compliance, and outcome reporting.
The full operations engagement. Essentials plus one new build per month, a governance check, a quarterly business review, and a rolling opportunity backlog. The tier where a partnership becomes a growth mechanism, not a maintenance arrangement.
For organisations with multiple active systems, cross-departmental deployments, or a fast-moving backlog. Two builds per month, active optimisation cycles, full EU AI Act documentation, and change orders at defined rates.
Technical capability often grows faster than adoption. Liorant bridges that gap by translating needs, structuring workflows, and helping business teams scale with technical teams.
In-house, freelancer, or a one-off project are all valid paths to a first deployment. They are not valid paths to a sustained AI operation. Here's where the gaps appear six months after launch.
The gaps in the first three columns are not failures of those approaches. They reflect a structural reality: monitoring, prompt management, cost tracking, governance, and adoption support are ongoing operational tasks. Every organisation eventually discovers this. The question is whether they discover it before or after a system degrades.
For CTOs, VPs of Engineering, and technical founders, AI Engineering as a Service means something more specific than dashboards and monthly reports. It means dedicated engineering capacity — AI architects and MLOps engineers working on your systems every sprint, on the tools you already use.
Retrieval-augmented systems from the retrieval layer up: chunking strategy, embedding selection, vector store config (Qdrant, pgvector, Pinecone), reranking, and evaluation against accuracy thresholds. We tune for retrieval quality and latency, not just cosine scores.
Multi-agent systems built with LangChain, LlamaIndex, and native APIs. MCP integrations connect agents to live business systems — CRMs, ERPs, document stores, proprietary databases. Handoff logic, guard rails, and failure modes are specified and documented before we build.
CI/CD for AI with MLflow, GitHub Actions, Docker, and Kubernetes. Evaluation runs on every deployment. Prompts are versioned, tested in staging, and rolled back when they regress. Drift detection runs on production output, not just at deployment.
Integrations beyond what no-code platforms support: direct API connections to CRMs, ERPs, proprietary databases, and third-party services, including legacy systems that need custom adapters. If it has an API, we can connect an AI system to it.
For Spain and the EU: risk assessments, data governance records, human oversight mechanisms, and audit logs structured for regulatory review. High-risk obligations apply from August 2026. Available on the Build & Optimise tier and as a standalone engagement.
Evaluation frameworks with LangSmith, Ragas, and custom harnesses — testing retrieval accuracy, hallucination rates, task completion, and edge-case failure modes. Production systems pass defined thresholds before deployment. We enforce that discipline from Phase 1.
The most common reason AI operations fail is not technical. It is the absence of a reporting layer that connects what the system does to what the business gains. We build observability in from day one — tracking what matters to a decision-maker, not what is easy to export from a dashboard.
Operational: credit consumption, connector health, token usage, prompt performance, model behaviour changes. Business: tasks completed, time saved per workflow, error rates before and after automation, volume processed. Operational observability keeps the systems running; business observability keeps the engagement commercially justified.
Monthly outcome reports state impact in language your finance team understands — hours saved, errors avoided, documents processed, leads qualified, connected to estimated value in euros. They are written documents your champion can take into a budget review, not dashboard exports. Quarterly reviews cover value delivered, open risks, and what's next.
Two things become possible. You can defend the investment internally with specific numbers rather than general impressions. And you can identify the next automation target with confidence, because you have visibility into which processes remain manual and what they cost.
Every AI system sits on a stack of moving parts: a model that receives updates, prompts that determine how it behaves, connectors that feed it data, and a cost structure that changes with usage. Without active management, any one of these layers can degrade your results — silently, and faster than most teams expect. Liorant manages all four continuously.
The prompts that govern your AI systems are operational assets, not set-and-forget configurations. We maintain a versioned prompt library for every active system — every change documented, tested before deployment, and linked to its observed performance outcome. Your team never inherits an unexplained black box.
Models do not perform identically across updates. We run a structured monthly evaluation: reviewing output samples against quality criteria, testing core scenarios, checking for accuracy drift. Where performance falls below the agreed threshold, the optimisation cycle triggers automatically — as a standing commitment, not on request.
AI usage has a cost structure that shifts with volume and platform version. We track credit consumption and API cost per workflow, per user, and per model, and report it alongside outcome figures — so the investment can be evaluated on a return basis, not just a spend basis. You do not need ML engineers on staff; we operate the technical layer, your team receives the output.
Review and version any changed prompts
Output quality, accuracy, drift detection
Credit consumption vs. budget, cost per workflow
Business impact in euros and hours, delivered
Then the cycle repeats.
An AI system that handles one process has operational value. An AI operations layer that covers multiple departments has strategic value. The difference is not the number of tools — it is the infrastructure that connects them, monitors them, and expands them in a disciplined sequence.
Illustrative. Actual results depend on scope and client process complexity.
Without monitoring and optimisation, deployed AI systems degrade. Platform updates change behaviour, connectors break, prompt drift reduces accuracy. The maintenance workstream exists to prevent a working system from silently becoming a broken one.
Each new build is delivered by a team that already knows your systems, data, governance requirements, and stakeholders. The onboarding cost of the first engagement is not repeated for the second, third, or fourth use case.
When a specific team is responsible for reporting AI results in business terms, the organisation maintains pressure on the deployment to deliver. The outcome reporting layer makes that accountability concrete and visible to the people who approved the investment.
The barrier to AI adoption is not the technology. It is the gap between a working system and the moment a non-technical user trusts it enough to change how they work. Marketing managers, legal assistants, operations coordinators, and finance analysts think about whether the system helps them work faster and more accurately. Liorant closes that gap — as a standing part of every engagement, not an optional add-on.
Most implementations stop here.
Office hours, prompt library, onboarding handoff.
Integrated into daily workflow — ROI becomes real.
Liorant operates across all three stages. Most providers only cover Stage 1.
Every system is built with the end user in mind: clear workflow design, documented behaviour, and a prompt library in language a non-technical user can immediately apply. Before Phase 1 closes, we run a structured handoff with the users — not just the sponsor. Adoption begins at handoff, not after it.
Adoption drops after the first few weeks without reinforcement. We run a standing monthly office-hours session for power users — thirty minutes to ask questions, request prompt improvements, and flag anything the system gets wrong. These sessions feed the following month's optimisation cycle.
When Copilot Studio ships a capability, Gemini changes how it handles a document type, or Claude updates a model version, your team needs to know what changed and whether it affects them. We deliver a targeted training brief whenever a material platform change occurs.
A monitored system, an active adoption programme, and a monthly build cadence mean a marketing, legal, or operations team can expand month by month without hiring engineers. They don't configure connectors, manage prompts, or review model changelogs. They use the results. Liorant handles the rest.
AI ENGINEERING AS A SERVICE
This is not the right engagement for every organisation. The following conditions make for a productive, high-value partnership.
Not sure where you fit? A 30-minute discovery session will tell you.
Book a free discovery sessionA managed service contract typically covers infrastructure or software maintenance at agreed SLA levels. AI Engineering as a Service covers the full operational lifecycle of your AI systems: monitoring, iteration, new builds, governance, reporting, and adoption. The output is expanding business value, not maintained uptime.
Every engagement includes monthly outcome reporting in business terms — hours saved, errors avoided, tasks completed, and estimated value in euros. Quarterly business reviews cover what was delivered, what it was worth, and what comes next. If the numbers do not justify the engagement, that becomes visible in the reports, and we address it directly.
Yes. Many clients begin at the Essentials tier after a Phase 1 activation and move to Care & Grow once the first deployment is stable and the backlog of additional use cases becomes clear. Tier changes take effect at the start of the next monthly cycle.
Our default delivery stack is Microsoft Copilot Studio, Google Gemini, and Claude — tools most SMB and mid-market clients already licence. For requirements that exceed what no-code platforms can deliver, we also build custom-coded systems with deep integrations, proprietary data pipelines, and RAG architectures. Platform selection happens during Phase 1 based on your existing infrastructure.
Phase 2 engagements carry a 30-day cancellation notice and a minimum initial period of three months. If you decide to bring AI operations in-house after Phase 2, Liorant delivers a documented handoff — the same governance, monitoring, and opportunity-backlog framework your internal team inherits. Many clients use the retainer period to build internal capability in parallel and then transition ownership when ready.
Yes. For technical buyers, AIEaaS works as an embedded engineering partnership rather than an outsourced replacement. We operate within your existing development workflow — your repositories, your sprint cycles, your communication channels — while contributing specialised AI engineering capability your team may not have in-house. Many engagements for technical buyers begin with a single architecture design sprint before moving to ongoing delivery.
Yes. Our default delivery stack uses no-code platforms for organisations that want results fast on tools they already licence. For requirements beyond that — custom RAG pipelines, multi-agent orchestration with MCP, proprietary data integrations, or production-grade model deployment — we build at the engineering layer using LangChain, LlamaIndex, MLflow, and cloud AI infrastructure on GCP, Azure, or AWS. Platform selection and scope are defined during Phase 1.
We spend thirty minutes understanding your team's workflows, existing tools, and priorities. At the end, you know whether Phase 1 makes sense and what it would cover.