Solutions · Use case
LLMOps Platform.
Generative AI, scoped to the problem it solves.
Production platform for prompt management, evaluation, observability, and cost control across LLM features.
Use-case context
Once you ship more than two or three LLM-powered features, the bespoke plumbing under each one (prompt versioning, evaluation runs, telemetry, cost attribution) costs more in maintenance than the features earn in lift. The fix is the lesson MLOps taught five years ago: platform-ise the shared plumbing before the per-feature drag gets unmanageable. We build LLMOps platforms that consolidate prompt and model versioning, evaluation runs, request-level telemetry and tracing, cost attribution, and policy gates (rate limits, content filters, PII redaction) behind a single SDK product teams consume. It runs on your own infrastructure and observability stack, and works with the model providers you already use. Measured by adoption (internal teams on the platform vs. hand-rolling), feature lead-time from prototype to production, and per-feature cost-per-request once the platform takes over the plumbing. Mean-time-to-detect (MTTD) on quality or cost regressions is the operational SLA. We do not build LLMOps platforms as black-box managed services. It runs on infrastructure you control, with full visibility into your data, models, and evaluation history. In our coverage footprint this lands first across technology: the sectors where the data shapes and evaluation criteria line up cleanly with what this use-case actually measures.
How this shows up across industries
Where llmops platform lands in production engagements.
Retail · Custom Software Development
See industry →For retail, custom software lands on the operational backbone: fulfilment, inventory, and merchandising tools that are too specific for off-the-shelf platforms.
Logistics · Custom Software Development
See industry →For logistics operators, custom builds typically sit on top of TMS or WMS platforms, automating exception flows and customer-facing tooling.
Education · Custom Software Development
See industry →For education, custom software handles enrolment, learning-management integration, and credentialing flows where platforms lock in institutional opinions.
Insurance · AI Integration Services
See industry →Insurance integrations route AI into claims and underwriting platforms with structured handoffs into adjuster queues.
Technology · AI Integration Services
See industry →Tech integrations are usually about exposing AI capabilities through internal platform APIs that other teams can consume safely.
Data shape
Prompt and model version history, evaluation gold sets and run results, request/response traces, cost telemetry from model providers, and the existing observability and CI stacks the platform must integrate with.
Delivering services
Where it lands first
Industries where llmops platform applies.
Where we draw the line
We do not build LLMOps platforms as black-box managed services. It runs on infrastructure you control, with full visibility into your data, models, and evaluation history.
Talk to us about a llmops platform engagement
A 30-minute call to scope where llmops platform moves the curve against your evaluation criteria.
Book strategy callWhy work with Veso AI on llmops platform
Measured
Evaluation, not opinion
Measured by adoption (internal teams on the platform vs. hand-rolling), feature lead-time from prototype to production, and per-feature cost-per-request once the platform takes over the plumbing. Mean-time-to-detect (MTTD) on quality or cost regressions is the operational SLA.
Proven
Quality you can measure
In the first two weeks we build a labelled evaluation set with your experts, so quality is measured from day one, not hoped for.
Production
Built to operate
We hand over working software, infrastructure-as-code, and an evaluation harness your team can run and maintain.
Related use-cases
Internal Copilots
Role-shaped copilots over internal knowledge: Confluence, runbooks, policies, code.
Search & Re-Ranking
Semantic and hybrid search with LLM-based re-ranking for retrieval-quality lift.
Evaluation Harnesses
Continuous, automated evaluation pipelines for production generative-AI systems.
FAQ
LLMOps Platform: frequently asked questions
How is success measured for llmops platform engagements?
Measured by adoption (internal teams on the platform vs. hand-rolling), feature lead-time from prototype to production, and per-feature cost-per-request once the platform takes over the plumbing. Mean-time-to-detect (MTTD) on quality or cost regressions is the operational SLA. The evaluation harness is part of the deliverable, not an afterthought. We build it during the engagement so your team can run it against the next prompt, model, or pipeline change without us.
Where does Veso AI NOT apply llmops platform?
We do not build LLMOps platforms as black-box managed services. It runs on infrastructure you control, with full visibility into your data, models, and evaluation history. This is a deliberate trust boundary, not a capability gap. We are equipped to build the systems we decline to build, and we decline to build them because the risk-to-value ratio in those surfaces does not justify it.
Which industries does llmops platform apply to?
In our coverage footprint, llmops platform most commonly lands in technology. The specific deployment shape varies by industry: data shapes, evaluation criteria, and regulators differ enough that we re-scope each engagement against the sector it lands in.
What data shape do you need to start a llmops platform engagement?
Prompt and model version history, evaluation gold sets and run results, request/response traces, cost telemetry from model providers, and the existing observability and CI stacks the platform must integrate with. In the first two weeks we look at the real data (what exists, what is labelled, where it has to live) and build the plan around what is actually there, not around an assumption.
Which Veso AI services ship llmops platform?
llmops platform ships under our Generative AI Consulting, Custom Software Development, Cloud Solutions Architecture service lines, depending on the integration surface and the build-vs-platform trade-off. Most engagements draw on more than one. The boundary between consulting, custom build, and integration is a scoping decision we make explicit during discovery.
How does a llmops platform engagement typically start?
We spend two weeks with your leadership and operators, build an evaluation set with your subject-matter experts, and come back with a clear plan and a clear price. That evaluation set anchors every decision after it (model choice, prompt strategy, retrieval design) so quality is measurable from week one, not from go-live.
Industries where llmops platform applies
Service lines that ship llmops platform