We take assistants, agents and language models from pilot to production on AWS. Your data never leaves your account, every answer is traceable and the cost per query is known before launch.
We start with the case that already has data available and a measurable return. An assistant that saves support hours is worth more than ten demos.
Answers with source citations over manuals, contracts, tickets or wikis. Permissions inherited from your systems: each person only sees what they could already see.
Agents that query your APIs, open tickets, reconcile data or prepare reports, with human approval where the risk demands it.
Invoices, contracts, medical reports or case files turned into structured data with validation and exception review.
A shared base so your developers can launch new cases in days: model access, evaluation, observability and spend control.
Every case is evaluated with several models on your real data. We compare quality, latency and cost per thousand queries before deciding.
| Task | Usual options | Decision criterion | Indicative cost |
|---|---|---|---|
| Reasoning and agents | Claude Fable 5.1, Claude Opus 5 via Bedrock | Quality on long tasks and tool use | High per query, low volume |
| High-volume chat and RAG | Claude Sonnet 5, Claude Haiku 4.5 | Latency and cost per thousand queries | Medium / low |
| Classification and extraction | Haiku 4.5, open models on SageMaker | Precision on your validation set | Low, scalable |
| Regulated data | Open models in your VPC, private endpoints | Data residency and compliance requirements | Fixed per instance |
Costs are calculated with your real volume during the assessment. No recommendation includes vendor commissions.
Inventory of use cases, available data and risks. We leave with one or two prioritised cases and their estimated return.
Working prototype on real data with an evaluation set. If quality does not reach the threshold, we say so here.
Deployment in your account as infrastructure as code, with guardrails, observability and spend control.
Quality and cost review, model updates and expansion to new cases with your team in charge.
Everything runs in your AWS account. Bedrock models do not train on your data and private endpoints avoid going out to the internet.
Every prompt, model or index change goes through a versioned evaluation set. No numbers, no deployment.
Budget per use case, spend alerts and prompt caching where it cuts the bill without losing quality.
Actions that affect customers, money or personal data carry human approval and a full log.
No. With Amazon Bedrock, input and output data is neither stored nor used for training. With open models on SageMaker, the model runs entirely in your account.
Not to start. Most cases are solved with available models, good data engineering and rigorous evaluation. We train your development team to maintain it.
It depends on volume and model. During the assessment we calculate the cost per query with your estimated traffic and propose the cheapest model that clears the quality threshold.
Yes. We choose a region in your jurisdiction, encryption with your KMS keys, private endpoints and access logging. We coordinate the impact assessment with your DPO when it applies.
We evaluate it in a free 45-minute session.
Describe the case, what data you have and who would use it. Within 48 hours we will tell you whether it makes sense and how we would approach it.
What we have written about this, from projects we have run.
Most teams pick a vector store from a benchmark chart and then discover the thing that hurts is metadata filtering, tenancy or reindexing. If you already run PostgreSQL, pgvector is usually the right answer for longer than people expect.
The cost per token looks obviously cheaper until you account for utilisation. GPUs bill by the hour whether or not anyone is asking questions, and that single fact decides most of these decisions.
A classifier on every request costs time and money on every request. What to check on input, what to check on output, and how to decide whether the guardrail is worth what it takes.