DoiT

doit.com

DoiT is a technology services provider with a mixed consulting, software and cloud-procurement business. Its engineering offerings range from technical assessments to collaborative production implementation, rather than advisory alone. AWS accelerators define specific deliverables and handover materials; its OpenAI, Anthropic and Forward Deployed Engineering practices provide engineers working alongside customer teams. AI Hub is a separate software offering, and cloud procurement addresses purchasing and billing. Buyers should establish which services, products and support arrangements are included in a proposed engagement. ^[1] ^[2] ^[3] ^[4] ^[5] ^[6] ^[7]

Services and delivery

AWS AI readiness and investment assessment

Advisory. Technical advisory for selecting viable AI investments. Work covers use-case prioritization, data and architecture readiness, security and team gaps, feasibility and ROI modeling. Deliverables include reference architectures, platform-selection analysis, an executive readout and a one-year roadmap. The assessment does not itself promise a production build. ^[1]

AWS generative AI implementation

Implementation. The AWS GenAI Accelerator takes a prototype toward production through architecture sessions, proof-of-concept deployment, foundation-model adaptation and pipeline implementation using Amazon Bedrock and SageMaker. Handover includes a working pipeline, deployment code, infrastructure-as-code templates, diagrams, documentation and an operating runbook. ^[2]

OpenAI application and agent engineering

Implementation. Engineers pair with customer teams to implement agents, workflows and retrieval-augmented generation systems in the customer’s codebase. Work includes model selection, evaluation harnesses, monitoring, guardrails, policy controls and inference-cost optimization, with validation against quality, cost and latency targets. ^[4]

Claude production engineering

Implementation. Embedded engineers help move Claude applications from pilot to production through Amazon Bedrock, Google Vertex or direct APIs. Scope includes model selection, reference architectures, automated evaluations, high-availability patterns, rollback planning, access controls and cost instrumentation. ^[5]

Cloud infrastructure optimization and operational engineering

Implementation. Forward Deployed Engineering provides hands-on cost optimization, Kubernetes resource and autoscaling tuning, incident diagnosis, migration support, and reliability and performance engineering. This is implementation and operational assistance; the supplied evidence does not establish a universal fully outsourced managed-service arrangement. ^[3]

Cloud security posture advisory

Advisory. Reviews cloud security posture and platform-native controls, including IAM, organization policies, service perimeters, key management and network segmentation. Buyers should specify which findings DoiT will remediate and which remain customer responsibilities. ^[3]

AI inference optimization and production validation

Implementation. The AI-specific portion of Forward Deployed Engineering covers inference-pipeline audits, token, model and GPU optimization, generative AI scaling and production validation. This is distinct from ordinary cloud cost engineering and from purchasing AI-cost software. ^[3]

AWS analytics pipeline implementation

Implementation. The Data to Insights Accelerator delivers an analytics pipeline using AWS Glue, EMR, Athena and QuickSight. Work includes proof-of-concept deployment, architecture reviews and training. Customers receive deployment code, documentation and infrastructure-as-code materials to operate their own platform. This offering alone does not establish AI or machine-learning delivery. ^[8]

AI Hub software

Software product. AI Hub provides AI-cost visibility by task, team, user and repository, together with spending limits, model rules and gateway functionality. It is a software product; its purchase alone is not an AI consulting engagement. ^[6]

Cloud procurement and billing support

Non-technical service. Supports cloud purchasing, commitment and discount guidance, and consolidated billing. The procurement offering also references optimization products and embedded engineers, but purchasing support should be distinguished from separately scoped technical implementation. ^[7]

Company details

Engagement and delivery

The AWS GenAI Accelerator uses engineers working alongside the customer team and specifies a technical handover. Its page distinguishes project delivery from continuing technical support, which is available through a Cloud Solve subscription. The Data to Insights Accelerator explicitly leaves the customer owning and independently operating the platform. ^[2] ^[8]

The AWS AI Assessment is described as a joint DoiT/AWS engagement, with AWS funding available for eligible customers. Funding eligibility and the boundary between assessment recommendations and subsequent implementation need confirmation for each purchase. ^[1]

Track record

For Promptly, DoiT reports co-developing patient-communications AI using secure model-execution pipelines, API integration, prompt chaining, validation layers, guardrails and monitoring. DoiT says the collaboration compressed a three-to-four-month development timeline into weeks. This is a vendor-reported project outcome, not an independently verified performance or healthcare-safety guarantee. ^[9]

For Finlex, DoiT describes helping evolve a LangGraph prototype toward an architecture using Bedrock AgentCore and Anthropic Claude. Work included secure multi-tenant identity propagation, structured model evaluations, document-processing redesign with Bedrock Data Automation, and agent and memory patterns. Delivery responsibility was shared with Finlex. ^[10]

For Software Projects, DoiT reports delivering AWS MAP Assess, Mobilize and Modernize phases, migrating 40 servers and consolidating two IBM Cloud regions into a multi-Availability-Zone AWS VPC while preserving application topology. The account also describes engineering a private network path between IBM Cloud Infrastructure and AWS. ^[11]

Scope and limitations

The supplied research did not reliably inspect detailed pricing or the Claude-on-Bedrock Accelerator’s scope. Their commercial terms and deliverables remain unverified.
Operational assistance is documented, but a universal fully outsourced managed-service contract and specific response-time commitments are not established.
The OpenAI practice page establishes an engineering offering but does not provide a named OpenAI-specific customer delivery reference.
Case-study outcomes are DoiT’s reports. Finlex’s differing savings figures and time frames have not been combined into a single outcome.

Questions to ask

Which production changes will DoiT own, and which implementation tasks remain with our team?
What acceptance criteria will apply to AI quality, latency, cost, security and deployment readiness?
Which model providers, deployment paths and platform constraints are included in the proposed scope?
Who owns the delivered code, evaluation assets, documentation and infrastructure, and what handover is included?
Is post-launch support included, or does it require Cloud Solve or another agreement? What response commitments apply?
Can DoiT provide production references matching our use case and deployment platform, and confirm any AWS funding eligibility?

Overview