Your AI pilot works. Now make it a product.
ThinkXL takes AI from demo to production — the architecture, the deployment, and the unit economics that hold up at scale. On AWS or your own infrastructure.
- AWS Solutions Architect Professional
- FinOps Certified
- 10+ years on AWS
$ terraform apply
Apply complete! Resources: 24 added, 0 changed, 0 destroyed.
✓ inference gateway live — eu-central-1
$ make cost-report
chat-assistant $0.011/req ▇▅▃▂▁ −38%
doc-extraction $0.034/doc ▅▄▃▃▂ −21%
✓ unit economics: within budget
pilot → production
- Architecture review
- Terraform apply 24 resources
- Security & IAM posture
- Monitoring & runbooks
- Production live
01
From Pilot to Production
Most AI initiatives stall between the demo and the product. We do the unglamorous work that gets you across: production architecture, security, automation, and a system your team can actually operate.
- Production architecture for AI products — built for reliability, security, and scale
- Hands-on build alongside your engineering team, from first commit to live traffic
- Amazon Bedrock, SageMaker, and self-hosted model deployment
- Infrastructure as Code (Terraform/CDK) — nothing lives only in a console
- Runbooks, monitoring, and knowledge transfer so your team owns what we build
cost per request — last 90 days
- chat-assistant $0.011 / request
- doc-extraction $0.034 / document
02
AI Unit Economics
An AI feature that loses money is a liability. We instrument your AI spend down to the feature and the user, then optimize until the economics work.
- Cost per user, per feature, per workflow
- Token cost attribution grounded in real billing data (AWS CUR, Bedrock)
- Model right-sizing — matching model capability and price to each task
- Spend anomaly detection and budget guardrails, before the surprise invoice
- FinOps reporting that ties AI spend to revenue and product outcomes
AWS managed
- Bedrock
- SageMaker
- ECS / EKS
Self-hosted
- Dedicated GPUs
- Hetzner
- Bare metal
chosen by economics, not by habit
03
The Right Infrastructure
We're AWS specialists who will tell you when AWS isn't the answer. Managed services, self-hosted models on dedicated GPUs, or a hybrid of both — we recommend what your economics support, then build it.
- AWS-native: Bedrock, SageMaker, ECS/EKS — with VPC, IAM, and security posture done right
- Self-hosted inference on Hetzner or bare metal when your usage justifies it
- Honest build-vs-buy analysis: managed convenience versus owned economics
- Architectures that keep you portable instead of locked in
knowledge graph
MATCH (c:Customer)-[:SIGNED]->(k:Contract)
04
Reliable AI with Context Engineering
When 'mostly right' isn't good enough, knowledge graphs make LLM systems consistent, explainable, and grounded in your actual data. We design and deploy GraphRAG in production — not in a notebook.
- Domain ontology and knowledge graph schema design
- Production graph database deployment — Amazon Neptune or Neo4j
- Entity extraction pipelines that turn your documents into a living graph
- GraphRAG retrieval wired into your application, alongside existing vector stores
Stuck between a promising pilot and a real product?
Book a 30-minute call with Pratik. No pitch deck, no pressure — just an honest read on what it would take to get your AI into production, and what it should cost to run.
Book an intro call