Claude implementation partner

Put Claude into production with control and visibility.

We take you from discovery and proof of concept to a production rollout and ongoing operations. Every build runs on a platform with one gateway for keys, budgets and routing, and full tracing of every prompt, cost and answer.

Book a scoping call See the services

Discovery to operations LiteLLM gateway Langfuse tracing

Services

One partner from first idea to day-to-day operations.

We run the whole Claude journey with you. Underneath it sits a production platform of LiteLLM and Langfuse, which we also deliver on its own.

Core service

Claude implementation, end to end

We find the use cases worth building, prove them quickly, take the winners into production, and keep them running well. One team, one plan, no handoff gaps between strategy and engineering.

Plan your Claude rollout

DISCOVERFind the right use casesWorkshops, value and feasibility ranking, data and security review.

PROVEProof of conceptA time-boxed build on your top use case, scored against agreed success metrics.

IMPLEMENTProduction buildIntegrations, agents and tools, security, and the LiteLLM and Langfuse platform.

OPERATERun and improveMonitoring, cost control, prompt and eval tuning, model upgrades, support.

Platform services

The foundation under every Claude app.

Most teams get a first Claude app working quickly. The trouble starts when ten teams, three environments and a finance review show up. These two services are how we deal with that, and you can buy them separately.

Gateway

LiteLLM implementation

A single, OpenAI-compatible endpoint in front of Claude and any other model you use, so apps stop holding provider keys.

Virtual keys per team and app. Revoke or rotate one without touching the others.
Budgets and rate limits. Spend caps by key, team or customer, with alerts before the cap.
Routing and fallbacks. Spread load across Anthropic API, Bedrock and Vertex AI, and fail over when one is down.
Guardrails and caching. PII masking, policy checks and response caching where it helps.
Production deployment. Containerised, behind SSO, backed by Postgres and Redis, defined in Terraform or Helm.

Observability

Langfuse implementation

See what your Claude apps are actually doing: which prompts ran, what they cost, how long they took, and whether the answer was any good.

Tracing for apps and agents. Nested spans for retrieval, tool calls and model calls, tied to a user or session.
Prompt management. Version, label and roll back prompts without a code release.
Evaluation. Datasets, LLM-as-judge scoring and human review queues to catch regressions before users do.
Cost and latency dashboards. Per model, per feature, per customer.
Self-hosted or cloud. Data residency and access control set up to match your security review.

Better together. We connect LiteLLM to Langfuse so every gateway request lands in a trace with its key, team and cost attached. One place to answer "who spent this, and was it worth it?"

Ask about the combined setup

How we work

Four stages, each with something you can use at the end.

You can stop after any stage. A discovery that shows a use case is not worth building is a good result.

STAGE 1

Discover

We map your processes, data and constraints, run workshops with the teams who would use Claude, and rank ideas by value and effort.

You get: a ranked roadmap and a business case.

STAGE 2

Prove

We build a working proof of concept for the top use case on real data and test it against success measures you agreed up front.

You get: a working PoC, eval results, a go or no-go.

STAGE 3

Implement

We build for production: integrations, agents and tools, security review, plus the LiteLLM gateway and Langfuse tracing. Then we roll out in steps.

You get: a live system, runbooks, a trained team.

STAGE 4

Operate

We watch quality, cost and uptime, tune prompts against your evals, handle model upgrades, and review results with you every month.

You get: steady operations and a support plan.

Platform packages

Start small, grow when the usage does.

These cover the LiteLLM and Langfuse platform work. Full Claude implementation engagements are scoped after discovery. Scope and timelines depend on your cloud, security rules and number of apps.

Package 1

Foundation

One gateway or one tracing setup, done properly.

LiteLLM or Langfuse in your cloud
SSO, keys and access roles
One application connected
Handover session and runbook

Best for a team shipping its first production Claude app.

Package 2 · Most requested

Platform

LiteLLM and Langfuse, wired together.

Gateway and tracing deployed as one platform
Budgets, routing, fallbacks and guardrails
Prompt management and an initial eval set
Up to several apps migrated
Cost and quality dashboards

Best for companies where several teams use Claude and finance is asking questions.

Package 3

Managed

We run it so your team builds features.

Upgrades, patching and scaling
Monitoring, alerting and on-call options
Monthly spend and quality review
Eval and prompt tuning support

Best after Platform, or when you have no spare platform engineers.

Platforms

Runs where your Claude already runs.

We deploy into your own cloud account, so prompts and logs stay inside your boundary.

Claude access

Anthropic APIAmazon BedrockGoogle Vertex AI

Where we deploy

Google CloudAWSAzureKubernetesCloud RunTerraformHelm

Fits with

PostgresRedisOpenTelemetrySSO / OIDCPythonTypeScript

FAQ

Questions we hear first.

We have no Claude use case yet. Can you start from zero?

Yes. That is what discovery is for. We work with your teams to find where Claude pays off, then prove one idea before anyone commits to a full build.

Can we buy only the platform work, without the full engagement?

Yes. LiteLLM and Langfuse are available as standalone packages, and you can add discovery, proof of concept or operations later.

Do we need both LiteLLM and Langfuse?

No. They solve different problems. LiteLLM controls access and spend; Langfuse shows behaviour and quality. Many clients start with one and add the other. They work well together, which is why Platform bundles them.

Will our prompts and data leave our environment?

Not if you do not want them to. We deploy both tools in your own cloud account and keep traces in your own database. Langfuse also offers a hosted cloud option if you prefer that.

We use Claude through Bedrock or Vertex AI. Does that work?

Yes. LiteLLM supports the Anthropic API, Amazon Bedrock and Google Vertex AI, and can route between them. That is a common way to add capacity and failover.

Does a gateway add latency?

A small amount, usually negligible next to model response time. We size and place it close to your apps and test it under your expected load before cut-over.

Can you work with an app we have already built?

Yes. Most projects start from existing apps. Moving to the gateway is mostly a base URL and key change, and tracing is added with a few lines per service.

Who owns the setup at the end?

You do. Infrastructure code, dashboards and runbooks live in your repositories. Managed support is optional.

Contact

Tell us what you are running on Claude.

A short note is enough. We will reply with questions and, if it fits, a time for a scoping call.

hello@stratigiklabs.com

Write to us

Helpful to include

Which Claude apps are live or planned
Where Claude runs today (API, Bedrock, Vertex AI)
Your cloud and any security constraints