Cost spikes nobody owns
Agent loops, retry storms, runaway tool calls. Without FinOps and rate controls at the gateway, your invoice is the first metric that tells you something’s wrong — and by then it’s already wrong.
Expertise / MLOps & AI Infrastructure
Run AI like you run production. Not like a science project.
Most enterprise AI infrastructure was built for one model and one workload. By 2026 you’re running models, agents, retrieval, and adapters across multiple providers — with continuous evaluation, FinOps, and audit. We design and operate the converged stack — gateway, observability, evals, vector, training, runtime — that makes AI a production system your team can actually run.
Built by senior teams from the University of Edinburgh, Amazon, Stanford & the Alan Turing Institute
The gap
Why prototype infrastructure breaks in production.
A working prototype is one model and one prompt. A production AI system is a stack — gateway, evals, observability, vector store, training, runtime — each with its own lifecycle. Without the stack, you can’t roll back a prompt, control cost across providers, catch a regression, or answer a regulator. Deloitte projects 50% of enterprises using generative AI will deploy agents by 2027 — which makes the converged stack the difference between scaling and stalling.
No structured tracing
When something fails, you can’t replay it. Tool calls, retrievals, prompts, model responses — without lineage across all of them, debugging is guessing and your incident reviews are theatre.
Evaluation drift
Prompts change, models update, indexes refresh. Without a continuous eval harness tied to release gates, regressions ship — and you find out from the user, not the system.
Audit gaps
ISO 42001, EU AI Act, sector regulators — they ask for model lineage, decision logs, and red-team evidence. Prototype stacks don’t have answers. Production stacks do.
How we build
The converged stack.
Six layers that turn AI from a prototype into a production system — engineered to operate, not to demo.
- Model gateway. TrueFoundry, Portkey, LiteLLM, or cloud-native (Bedrock, Vertex). Routing, policy, cost control across OpenAI, Anthropic, Google, and open models — under one control plane.
- Observability & evals. LangSmith, Langfuse (now ClickHouse-owned), Arize Phoenix, Datadog LLM Observability, Braintrust. Continuous evaluation tied to CI/CD — not a launch-week checkbox.
- Vector & retrieval. Weaviate (Agent Skills, Feb 2026), pgvector, Pinecone, Qdrant, Vespa for billion-scale. Selected for your residency, latency, and operating cost — not the loudest benchmark.
- Data & training. Databricks Mosaic, Snowflake Cortex, Ray/Anyscale for distributed training. ClickHouse for analytics. The pipeline that feeds the stack, on the platform you already run.
- Agent runtime. LangGraph, CrewAI, or custom orchestration. Where agent loops are observed, logged, and rate-controlled — with an audit trail for every action.
- FinOps & governance. Per-tenant, per-feature, per-model cost attribution. Audit-grade logs. Rollback for prompts and adapters. The spine, not a side project.
Six layers, one converged stack — with FinOps and governance running through every layer, not bolted on at the end.
What we ship
Infrastructure patterns.
Three patterns we ship, each engineered for a different starting point.
01
Greenfield AI platform
Stack stand-up for organizations moving past the first model into a platform. Gateway, observability, evals, vector, training, runtime — designed to your residency, your scale, your providers. Production from day one, not a hand-off in six months.
02
Platform consolidation
Multiple teams, multiple stacks, no unified observability. We converge them — one gateway, one eval harness, one tracing model, one cost view. Without breaking what’s already shipped.
03
AgentOps build-out
The agent-specific observability, evaluation, and governance layer on top of an existing AI platform. Rate controls, loop detection, structured traces across tool calls — so the agents you’ve already shipped can keep operating safely.
What we ship
What we engineer around the stack.
The stack is the surface. These are the layers that make it operate.
01
Continuous evaluation harness
Eval suites tied to CI/CD. Regression tests on every release. Domain-specific scorecards, golden sets, and production telemetry that catch the drift before the user does.
02
FinOps & cost attribution
Per-tenant, per-feature, per-model cost visibility. Policy at the gateway — not after the fact. Engineering teams see what they spend; finance gets a number they can defend.
03
Audit-grade tracing
Structured logs for every prompt, tool call, retrieval, and response. Lineage from request to model to output. The evidence trail a regulator asks for — already there when they ask.
Where it earns its keep
Infrastructure that earns its keep.
Where the converged stack pays for itself — six places it turns AI from a fragile pilot into a system your team operates with confidence.
✓
AI platform stand-up
Greenfield enterprise AI platform with the full converged stack — gateway to governance, designed to operate from launch.
✓
Stack consolidation
Multiple AI initiatives, one platform. One gateway, one eval harness, one cost view — without breaking what’s already shipped.
✓
Cost optimization & FinOps
Per-team, per-feature, per-model attribution. Policy at the gateway. The bill stops being a surprise.
✓
Eval & observability rollout
Continuous evaluation tied to release gates. Production telemetry that catches drift before users do.
✓
AgentOps build-out
Agent-specific observability, rate controls, structured tracing, and audit — bolted onto an existing AI platform.
✓
Audit-readiness program
ISO 42001, EU AI Act, and sector regulator alignment for the AI stack — model lineage, decision logs, red-team evidence already in place.
FAQ
Questions we get from heads of platform.
Have MLOps, LLMOps, and AgentOps actually converged?
In practice, yes. The labels still exist, but the stack underneath is one: gateway, observability, evals, vector, training, runtime, FinOps. Teams that treat them as separate disciplines end up with three half-built platforms and no unified cost or audit view. We build the converged stack from the start.
Do we need a model gateway if we’re only using one provider today?
Usually yes — but for the controls, not the routing. The gateway is where you enforce sanctioned-model lists, per-tenant rate limits, cost attribution, and audit logging. Even on one provider, that’s where your governance lives. And by the time you add a second provider — which most teams do within a year — the gateway is already there.
Which observability tool — LangSmith, Langfuse, Arize, Datadog, Braintrust?
It depends on what you already run. If you’re a Datadog shop, Datadog LLM Observability earns its keep on integration alone. If you want open-source with the eval workflow built in, Langfuse (now ClickHouse-owned) is the strong default. Arize Phoenix and Braintrust are excellent where eval rigor is the priority. We pick on operating fit, not vendor preference — and we’ve shipped all of them.
How do we attribute AI cost by team, product, or customer?
At the gateway, with tagged requests and per-tenant policy. Every call carries the tags it needs — team, feature, customer, model — and the gateway emits the cost record. From there it lands in your FinOps tooling. The mistake teams make is trying to reconstruct attribution after the fact from provider invoices. It doesn’t work.
What does “audit-ready AI infrastructure” actually mean?
Three things, concretely: model and prompt lineage you can replay, decision logs for every agent action, and red-team evidence tied to releases. ISO 42001 asks for the management system; EU AI Act asks for the technical file; sector regulators ask for the audit trail. Audit-ready means all three answers exist before the question does.
Do you build it, or do you operate it?
Both, on your terms. Most engagements ship with a 30–90 day operating handoff — your team runs the stack, we stay on for SLA-backed support if you want it. The code, the configs, the eval suites, the dashboards — yours. We don’t lock the door behind us.
Where to start
Where to start.
Bring us your current AI infrastructure — or your plans for one.
We audit the converged stack against production criteria most teams skip: gateway controls, observability coverage, evaluation harness, retrieval design, training pipeline, agent runtime, FinOps, and audit. You leave with a scored gap analysis and a sequenced build-or-consolidate plan, with vendor recommendations matched to your scale and constraints.
What you get: a scored production-readiness assessment across the eight stack layers; a target architecture for your environment, with vendor recommendations; a staged delivery plan with timelines and effort estimates; and one workshop with your platform and AI engineering leads. Led by a senior consultant — fixed scope, fixed fee.
Book an Infrastructure Review →Start the conversation
Ready to run AI like a production system?
A 30-minute conversation with a senior consultant. Bring your current AI stack — or the plan you’re drafting. We’ll tell you where it’ll hold up, where it’ll break, and what an Infrastructure Review would surface.
Book an Infrastructure Review →