A shared LLM platform, in your account, that every team can build on.
Once a second team wants to use a model, you have a platform problem: who pays, who approves, which version is live, and how anyone proves what it did. We build that layer on Amazon Bedrock so the tenth use case is cheap instead of another project.
The second team is where the trouble starts.
The first AI feature ships with its API key in a secret, its prompts in the application repository and its costs buried in one line of the bill. That works exactly once. By the third team you have three approaches to safety, no idea which prompt is in production, and a finance question nobody can answer.
Costs nobody can attribute
One invoice, several teams, no tags. When spend doubles, the only available response is to ask everyone to be careful.
Prompts as untracked configuration
A prompt is production logic. Kept in a code comment or a spreadsheet, it changes without review, without a version and without anyone able to say what behaviour changed with it.
No way to answer “why did it say that?”
Without request tracing that links input, retrieved context, model version and output, an incident review is guesswork, and a regulator's question has no answer.
Six capabilities, shared across every team.
Model access & routing
One endpoint for your teams, several models behind it. Route by task, cost or latency, fail over when a provider degrades, and change models without changing every application.
Prompt & version control
Prompts and their configuration held as versioned artefacts with review, staged rollout and rollback. A change to a prompt goes through the same gate as a change to code.
Cost attribution & budgets
Every call tagged to a team, a feature and an environment. Dashboards per owner, budget alerts before the surprise, and enough detail to decide whether a use case is worth its bill.
Guardrails
Content filters, refusal policy, PII detection and redaction, injection defences and per-tenant rate limits, applied centrally so no team has to reinvent them badly.
Tracing & observability
Every request recorded with its prompt version, retrieved context, model, latency, token count and outcome, with retention set to your policy. This is what makes an incident review possible.
Evaluation in the pipeline
Golden sets run automatically on model or prompt changes, with a quality gate in the deployment pipeline rather than a hopeful manual check.
MLOps & governanceSix to ten weeks to a platform two teams are using.
Landing
Accounts, networking, identity, regions and data boundary. If your AWS landing zone needs work first, we say so before building on top of it.
Core
Bedrock access, the gateway, routing, tagging, guardrails and tracing, deployed as infrastructure code in your repositories.
First tenant
Onboard one real use case end to end, so the platform is proven by a workload rather than by a diagram.
Open it up
Self-service onboarding, documentation, budgets, alerting and a runbook, so the next team does not need us at all.
Because the interesting part is not the model.
Bedrock gives you several model families behind one interface, inside your own account and your own VPC, with your data staying within your boundary. That means the platform outlives any particular model choice — which matters, because the model you would pick today is not the one you will be running next year.
Questions we get asked
Are we locked into Bedrock or into AWS?▼
No. The gateway, routing and evaluation layers are ours to build and yours to keep, and they are deliberately provider-agnostic. We have built the same pattern on Azure OpenAI and on Vertex AI. Bedrock is a good default when you are already on AWS, not a conviction.
Does our data train anyone's model?▼
Not through this design. Inference runs inside your account, within the region you choose, and prompts and completions are retained only in the trace store you own, under the retention policy you set. That property is worth verifying in your own contract terms as well, and we will help you do that.
Can it stay inside the EU?▼
Yes. Model endpoints, the trace store, the knowledge layer and the logs can all be pinned to EU regions, and we document where each one lives so the answer survives an audit.
What does it cost to run?▼
Two parts: inference, which is proportional to use, and the platform itself, which is mostly serverless and usually modest next to the inference bill. We build the cost model in the first fortnight so that the number is known before it is spent.
We already have one team using an API key directly. Is that wasted?▼
No. Migrating an existing application onto the gateway is normally a small change, and it is the fastest way to prove the platform. We would use that team as the first tenant.
One free hour. No pitch, no obligation.
Bring the problem you are stuck on — an integration that keeps breaking, a cloud bill nobody can explain, an AI project that is all demo and no product, or a platform you are about to invest in. You will leave with a straight answer and a written summary, whether or not you ever work with us.
- Architecture and integration review
- AI feasibility — what will actually work, and what will not
- AWS, Azure and Google Cloud cost and design
- Business process audit and ISO readiness
- Technical due diligence before you invest or acquire