AI Solutions
We make AI a real product capability, not a science project — Bedrock for foundation models, SageMaker for custom training and fine-tuning, and the evaluation, guardrails, and cost controls that make it production-ready.
The state of most AI initiatives
- A demo works once, in a notebook — but never makes it to a user-facing flow
- No evaluation pipeline — nobody can answer "is the model still working as well as it was last week?"
- Cost runaway is the default — token spending grows faster than usage because nothing caches and nothing batches
- Guardrails are either missing or copy-pasted from a blog post — not designed for your specific use case
- "Use Bedrock" is the architecture diagram — the integration, retry, fallback, and observability story doesn't exist yet
- Product, engineering, and security each have a different definition of "shipped" — and the gap is widening
The gap between AI demo and AI product
A demo is a thirty-second showcase under ideal conditions. A product handles edge cases, abuse, cost, latency targets, regression, and customer-specific data — every day, at scale.
The gap is mostly infrastructure: evaluation, observability, guardrails, caching, and routing. We close that gap on AWS-native primitives.
From proof-of-concept to production AI
- Bedrock integration — Claude, Titan, and open-source models
- SageMaker for custom model training and fine-tuning
- Inference endpoint optimization — Serverless and Real-Time
- RAG architecture — knowledge bases, embeddings, vector search
- Guardrails, evaluation pipelines, and production monitoring
- Cost optimization — model routing, caching, batching
AI Stack on AWS
Ship AI that actually works in production
Use-Case Scoping
We identify the highest-value AI use cases for your product, score them by feasibility and business impact, and recommend an approach.
Architecture Design
Model selection, RAG architecture, guardrails, and evaluation strategy. We design the full system before writing any code.
Build & Iterate
We build iteratively — get something working end-to-end first, then optimize. Every sprint ships a working feature, not a prototype.
Production Readiness
Before launch: evaluation pipelines, cost monitoring, guardrails, and a runbook. We don't hand off code that isn't production-ready.
What you walk away with
- A working AI feature in production — with retries, fallbacks, and the observability to debug it at 2 AM
- An evaluation pipeline that runs on every deploy — so you know if quality is improving or regressing
- Cost monitoring and model routing — cheap models for easy queries, expensive ones only when they earn it
- Bedrock Guardrails configured for your specific safety, PII, and topic boundaries
- A runbook the on-call engineer can use without a PhD — covering common failure modes and how to triage them
- A team that can extend the feature — prompt iteration, eval suite expansion, and new use cases — without us
AI is not a feature. It's infrastructure.
We'll help you scope a realistic AI initiative, avoid the common pitfalls, and build something your users will actually notice.
Talk to Us About AIFrequently asked questions
Should we use Bedrock or build our own model infrastructure?
For most product features, Bedrock: managed foundation models, no GPU fleet, guardrails and knowledge bases built in. SageMaker earns its complexity when you genuinely need custom training or fine-tuning. We default to the smallest stack that ships your use case.
Is our data used to train the models?
No — AWS does not use your Bedrock inputs and outputs to train the base models, and your data stays in your account and region. We design the architecture so prompts, embeddings, and knowledge bases live inside your VPC boundary and your IAM controls.
How do you keep an AI feature from degrading after launch?
An evaluation pipeline that runs on every change: a test set of real prompts scored against expected behavior, so quality regressions show up in CI rather than in customer tickets. This is the single most-skipped piece of production AI — and the first thing we build.
What about hallucinations and off-topic output?
Retrieval grounding (RAG) narrows the model to your data; Bedrock Guardrails enforce topic and safety boundaries; and the eval suite measures how often answers cite retrieved context. No system is perfect — the goal is measured, bounded behavior instead of vibes.
What drives the cost of an AI feature?
Token volume, model tier, and context size. The architecture controls all three: caching for repeated queries, routing easy requests to cheaper models, and trimming retrieval context. Cost monitoring per feature is part of the production-readiness checklist, not an afterthought.
What does an engagement cost?
Fixed scope and fixed price per phase, set after use-case scoping — the same model as every engagement we run. The AWS runtime costs (the question below) are separate and yours; we design to keep them predictable.
How long until something is in production?
The first working end-to-end slice typically lands within the first sprint or two — then we iterate on quality against the eval suite. What we do not do is spend a quarter on architecture before anyone sees output.
Related: Well-Architected Review · DevOps & CI/CD · AWS Modernization