From a prioritized use-case shortlist to agents running in production — architected, guarded and funded on AWS.
WHY AWS
Amazon Bedrock serves fully managed models from the labs that build them, behind one API, in the AWS region you choose. Your prompts and completions are never used to train the underlying models, and switching model is a config change, not a migration — with a private VPC endpoint, nothing you send has to leave your network to get there.
ASSESS
Our free AI Use Case Lab runs the first pass on its own, in about ten minutes:
BUILD
Dealership financing paperwork read, classified and checked for completeness before a human sees it.
Bedrock Data Automation blueprints, a Main Agent with Lambda tools, Guardrails, EventBridge, Slack delivery
Dily
KYB due diligence that cross-references external registries and compliance APIs, and shows its working.
Blueprint Builder Agent, a Master Agent with web-search and compliance-API tools, Knowledge Bases, Guardrails, Glue + Athena
Akua
Clinical notes audited against standard and facility rules, multi-tenant, with an auditable report per note.
Step Functions — classify, prescreen, global and facility rules, longitudinal analysis, report builder — Cognito, API Gateway, Lambda, Transfer Family, DynamoDB
MedrecAI
A healthcare client's own agent build, advised end to end — from data foundations and agent infrastructure to tools, knowledge bases and production-ready agent logic.
Bedrock, Step Functions, Lambda, S3/DynamoDB/RDS, vector stores (OpenSearch, Redis)
Yedahealth
Pathology images de-duplicated, segmented and routed for annotation, with PHI stripped before any of it.
SageMaker inference endpoints, pgvector on RDS, SQS to EventBridge Pipes to Step Functions, Glue ETL
Vistapath
OPERATE
Bedrock Guardrails on every agent, plus prompt-injection and data-leakage testing as a scoped deliverable rather than an afterthought.
We read the agent as an attacker would, then hand back a Well-Architected matrix, severity-ranked findings and a sprint-ordered remediation plan. On one recent MVP that meant seven critical findings before a single patient record moved.
PHI stripped before anything reaches the analytics account. PII and non-PII in separate buckets and separate accounts, customer-managed KMS keys, least-privilege IAM.
An agent is only as sound as the account it runs in. Landing Zone, IAM Identity Center, CloudTrail, GuardDuty, Config, Security Hub, private networking, budgets and billing alarms — all as code.
Some clients want the capability in-house. We design the target architecture, set the standards, review the pull requests and run the demos — your engineers ship it.
AI, agent and data engagements delivered — document processing, KYB, clinical audit, conversational care, computer vision.
Countries: the United States, Uruguay, Chile, Mexico and Colombia.
AWS funding programs contributed to every one of these engagements — and covered the full cost on three.
Unlocking the immense potential of Generative AI is essential for both startups and enterprises. To meet this challenge, binbash has developed a unique program offering founders and technical leaders expert guidance on deploying Large Language Models (LLMs) on AWS.
This framework covers critical areas such as model selection and tuning, decisions between training new models or using pre-trained ones, and hosting options including Amazon SageMaker and Amazon Bedrock.
Contact us to turn your idea into an LLM deployment plan.
