FAQ
Questions, answered.
What ClearThought does, who we work with, and how an engagement runs.
What ClearThought does, who we work with, and how an engagement runs.
ClearThought.ai is an independent AI, AWS, and security consulting firm that designs and deploys secure, high-performance AI systems on AWS, Azure, and Google Cloud. The work spans three disciplines: AI/ML engineering (LLM selection, fine-tuning, RAG architectures, and evaluation harnesses), cloud architecture (AWS-first reference architectures, IaC modules, and patterns for inference at scale, with Azure and Google Cloud where it fits), and security and compliance (zero-trust IAM, encryption strategy, and threat modeling for LLM-specific risks). The brand line is "Intelligence, Architected," and the output is engineering, not slideware.
It serves engineering organizations at mid-market and enterprise companies that need to design, build, or harden production AI systems on AWS, Azure, and Google Cloud. Engagements are principal-led and engineer-first, so they fit teams that want senior architects working directly on their systems rather than a staffing layer. Both technical evaluators and the executives sponsoring the work are common points of contact.
AWS is our primary platform and where we go deepest: Amazon Bedrock, OpenSearch, EKS, ECS, serverless inference, multi-account landing zones with Control Tower and Organizations, and data-residency design. We also deliver integrations on Microsoft Azure and Google Cloud, which suits multi-cloud estates and migrations. The firm is vendor-neutral on models and does not resell anyone's models.
There are three engagement models. An Architecture Sprint is two weeks: a focused review plus a reference architecture for a defined system or migration. An Embedded Build runs four to twelve weeks, with principal engineers working alongside the client team to design, build, and harden production systems, and Standing Advisory is a quarterly retainer for ongoing architecture review, security-posture monitoring, and on-call principal access.
Yes, everything is build-to-be-owned. Reference architectures, IaC such as Terraform or CDK, working pipelines, documentation, and runbooks are handed over to the client team. The approach is designed to avoid vendor lock-in so the client can operate and extend the systems independently.
Security is treated as a first principle, not an add-on. The work includes zero-trust IAM and least-privilege design, encryption strategy with KMS and envelope encryption, and threat modeling for LLM-specific risks like prompt injection and data leakage. ClearThought.ai also provides readiness and advisory services to help clients reach SOC 2, HIPAA, and ISO 27001; these are services that support the client's compliance goals, not certifications the firm holds.
No. ClearThought.ai is independent: not a reseller, not a body shop, and not a staffing shop. The firm is vendor-neutral and does not resell anyone's models, so recommendations are made on engineering merits. Work is principal-led with no layers and no handoffs.
Pricing follows the three engagement models rather than a one-size package. The Architecture Sprint is a fixed two-week scope for a focused review and reference architecture, the Embedded Build is a four to twelve week scope where principal engineers build alongside your team, and Standing Advisory is a quarterly retainer for ongoing review and on-call access. To scope an engagement and discuss structure, reach out at hello@clearthought.ai.
Yes. The Embedded Build model puts principal engineers alongside your team to design, build, and harden production systems together, and Standing Advisory gives your engineers on-call access to a principal for ongoing review. The intent is knowledge transfer and ownership, with everything documented and handed over so your team can run it after the engagement.
Quality is engineered, not assumed. The work includes evaluation and regression harnesses, LLM selection, fine-tuning where it helps, and RAG pipelines built on Amazon Bedrock, OpenSearch, and pgvector. Cost and latency optimization for high-volume inference is part of the same effort, so quality, performance, and spend are tuned together.
Yes. RAG and retrieval systems and LLM evaluation, guardrails, and red-teaming are core specializations within our AI/ML Engineering practice. We build retrieval pipelines (vector store selection, embedding and retrieval tuning, and grounding controls on Amazon Bedrock, OpenSearch, and pgvector) and the evaluation and regression harnesses that keep them honest in production: automated quality, hallucination, and safety checks, plus guardrails and red-teaming against prompt injection and jailbreaks. Amazon Bedrock cost and latency optimization is tuned in the same effort, so quality, performance, and spend move together.
Yes. AWS to Azure and AWS to GCP AI workload migration is a named specialization within Cloud Architecture. AWS is our primary platform, and our engineers hold certifications across AWS, Microsoft Azure, and Google Cloud, so we deliver Azure landing zones and Entra ID identity and GCP data platform delivery on BigQuery and Vertex AI alongside AWS landing zones and multi-account governance. That makes us a fit for multi-cloud estates, post-acquisition integration, and portability work where AI and data pipelines need to move or run across more than one cloud.
Yes, as readiness and advisory work, not as certifications the firm holds. SOC 2 readiness for LLM applications and HIPAA-readiness architecture for healthcare AI are specializations within Security and Compliance, alongside AI and LLM threat modeling for prompt injection and data exfiltration and cloud security posture management and IAM hardening. We map controls to real architecture decisions, with encryption strategy using AWS KMS and envelope encryption, so documentation follows the system rather than the other way around. These services support the client's compliance goals; the firm does not claim certification or partner status.