Your AI pilot works. Now make it production-grade on AWS.
ThinkXL prepares your AI product for real traffic — sound architecture, security posture, observability, and unit economics. We take care of the infrastructure, so you stay focused on the application.
$ terraform apply module.inference.aws_ecs_service.bedrock_gateway: Creation complete after 2m31s Apply complete! Resources: 24 added, 0 changed, 0 destroyed. Outputs: gateway_url = "https://ai.internal.acme.dev/v1" $ curl -s $GATEWAY/health | jq -r .status healthy $ python unit_costs.py --source cur,bedrock --window 30d feature requests $/request Δ 30d chat-assistant 412,008 $0.011 −38% doc-extraction 88,114 $0.034 −21% source: AWS CUR + Bedrock invocation logs
pilot → production
- Architecture review
- Terraform apply 24 resources
- Security & IAM posture
- Monitoring & runbooks
- Production live
01
Production Infrastructure & Architecture
Built a prototype on Lovable, v0, or a notebook? We put real infrastructure under it — secure, observable, automated, and ready for production traffic.
- Architecture for AI workloads — reliability, security, and scale from day one
- AWS-native: Bedrock, SageMaker, ECS/EKS — with VPC, IAM, and security posture done right
- Infrastructure as Code (Terraform/CDK) and automated deployments
- Cost-aware hosting for the app, data, and vector stores around your models
- Build-vs-buy guidance, and architectures that keep you portable
cost per request — last 90 days
- chat-assistant $0.011 / request
- doc-extraction $0.034 / document
02
Unit Economics & Cost Attribution
Know what your AI costs, down to the feature and the user. We instrument your spend across every feature, user, and workflow, then re-architect until the economics work — starting from billing data you already produce.
- Cost per user, per feature, per workflow — tied to product and revenue
- Token attribution from real billing data — AWS CUR, Bedrock logs, provider usage APIs
- Model right-sizing — matching capability and price to each task
- Inference-path optimization — prompt caching, routing, batching, and leaner context
- Budget guardrails and spend anomaly alerts
request trace — 1.24s
- guardrails 12ms
- retrieval 210ms
- prompt assembly 40ms
- model response 980ms
- post-process 55ms
03
AI Observability
See what your AI is actually doing. We build observability for the AI path itself — every prompt, retrieval, tool call, and response — so you can spot quality drift, latency, and cost as they happen.
- Tracing across the full AI path — prompts, retrievals, tool calls, responses
- Quality and drift monitoring — catch regressions early
- Cost and latency per request and per feature
- Abuse and anomaly detection — runaway sessions, prompt injection, denial-of-wallet
- Built on OpenTelemetry, added alongside your stack
knowledge graph
MATCH (c:Customer)-[:SIGNED]->(k:Contract)
04
Retrieval & Grounding
AI answers grounded in your own data. We build the retrieval layer — vector and knowledge-graph RAG, plus the data pipelines behind them — that makes your AI output consistent, explainable, and production-ready.
- Retrieval architecture — vector RAG, GraphRAG, or both, matched to your data
- Data and ingestion pipelines that keep your knowledge fresh
- Knowledge graph and ontology design for explainable answers
- Production graph and vector databases — Amazon Neptune, Neo4j, or your stack
- Wired into your application alongside what you already run
control plane — your cloud
Control plane
05
AI Control Plane
One place to route, govern, and control your AI traffic. We deploy a control plane between your application and the model providers, so routing, caching, spend control, and logging live in one place you own.
- Self-hosted in your AWS — your keys, your data, your infrastructure
- Routing and automatic fallback across Bedrock, Anthropic, OpenAI, and more
- Prompt and response caching, with per-user rate limits and budgets
- Guardrails — PII redaction, input limits, content filtering
- OpenAI-compatible — integrate by pointing to one endpoint
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