Home / Services / AI Integration & LLM Systems
Service 03 — AI Integration & LLM Systems

AI your auditor will sign off on.

Bedrock RAG that survived a Tier-1 bank's OSFI B-13 review in six weeks. If your data can't leave your VPC, we're built for you. If it can, we're probably overkill.

Engagement length
6—16 weeks
Data locality
Zero egress by default
Compliance
OSFI · SOC 2 · HIPAA
First deliverable
48 hours
What's includedThe work itself

RAG, agents, and LLM systems your regulator understands.

Four stages
of delivery
DeliverablesWhat you'll have at the end

A production AI system — not a demo you're afraid to ship.

i.

RAG system in prod

Ingestion, embeddings, retrieval, generation — all in your AWS account, all under your IAM.

ii.

PII control layer

Pre-model masking, reversible only for authorized roles. Documented mapping of every entity type we detect.

iii.

Eval harness

A test suite your team runs on every model or prompt change. Regression catches before deploys, not after.

iv.

Audit trail

Every prompt, every response, every retrieval — queryable for compliance. The evidence your auditor will accept.

v.

Cost controls

Per-team budgets, token limits, circuit breakers. We've stopped runaway cost bugs before they hit six figures.

vi.

Handoff sessions

Three weeks of pair-ops with your engineers and your compliance team. We answer the hard questions together.

StackWhat we build with

Built for VPC-bound data by default.

Models
Claude · Titan · Llama · Mistral
Platform
Amazon Bedrock · SageMaker
Retrieval
OpenSearch · Pinecone · pgvector
PII & safety
Comprehend · Bedrock Guardrails
Orchestration
Step Functions · Lambda · EventBridge
Evaluation
Ragas · LangSmith · Custom harnesses
Audit
CloudTrail · CloudWatch · S3
Frameworks
LangChain · LlamaIndex · DSPy
Case studyCanadian Tier-1 Bank · 2024 — present

We want AI. Legal says one byte of customer data off-prem and it's a newspaper story.

Enterprise AI platform cleared through OSFI B-13 in six weeks. Zero data egress.

The bank's internal team had built a RAG prototype on OpenAI's API six months earlier. It worked technically, but compliance had stopped the production rollout cold — the data was leaving the building, and no amount of TOS language was going to clear that with OSFI.

We rebuilt it on Bedrock with a multi-layer PII control pipeline, Claude as the reasoning model, OpenSearch for retrieval, and every prompt logged to an S3 bucket their auditor had read-only access to. Six weeks later it was serving 1,300 queries a day — and costing 61% less than the GPT-4 version would have.

A good fit if you —

Can't let your data leave your VPC.

Not a fit if you —

Just need a chatbot on your website.

ProcessFrom first call to handoff

Four stages. Plain rules at each one.

i.
Step 01

Discovery

48 hours to architecture + budget. If we can't deliver both in the same document, we refund the discovery fee.

ii.
Step 02

Architecture

Compliance reviewed before code is written. Your legal team signs off on data flow, PII handling, and audit posture.

iii.
Step 03

Build & evaluate

Ships in your AWS account. Eval harness runs on every change. We don't push to prod without it going green.

iv.
Step 04

Handoff

Three weeks of pair-ops with your engineers and your compliance team. Then we go.

QuestionsCommonly asked

The honest answers.

What buyers ask us before signing
How much does an AI engagement cost?

Scoped RAG systems: $80–180k. Full agent platforms with complex integrations: $200–400k. Compliance-heavy builds (OSFI, HIPAA) add 20–30% for the audit evidence work. You get the architecture plan and budget in the first 48 hours.

Why Bedrock over OpenAI / Anthropic direct APIs?

For regulated clients: data locality, VPC endpoints, zero-egress by default. For most others: Bedrock is a wrapper — we'll use whichever model serves the use case. We don't get paid by AWS to recommend them.

Can you help us decide if we need AI at all?

Yes — and we've told clients they didn't, on the first call. A good RAG system replaces a sharp SQL query 30% of the time. We'll point that out before the scoping document gets written.

What about hallucinations?

Evaluated against your actual retrieval corpus, not a generic benchmark. We report confidence scores per-answer and fail closed when retrieval quality drops below threshold. "The system doesn't know" is a valid answer.

Do you work with open-source models?

Yes — Llama, Mistral, and fine-tuned variants via SageMaker when the use case demands it. But most production RAG systems don't need fine-tuning; they need better retrieval. We'll tell you which applies.

Ready to ship AI your auditor will sign off on?

First call is thirty minutes, on a Tuesday or Thursday, with two engineers — not a sales rep. If we're the wrong fit, we'll name someone better.

Start a project → See the work
Service 01

AWS Architecture & Migration

The AWS environment you'll still be running in five years — built for the team that will inherit it.

→
Service 02

DevOps & CI/CD Pipelines

PR merge → multi-region prod in under 12 minutes, with the audit trail a regulator would accept.

→
Service 04

Big Data & Analytics

Lakehouses that serve engineers and external customers from the same pipeline. One source of truth.

→