Services
AI outcomes need data foundations and an operating owner
Our service architecture keeps AI delivery and data engineering distinct while connecting them through cloud platforms, integration, evaluation, and managed operations.
Explore use casesAI services
AI Advisory & Delivery
Choose, design, build, and evaluate a bounded AI workflow—from readiness and agents to voice, documents, and enterprise knowledge.
AI Strategy & Modernization Assessment
Assess workflows, low-code automations, data, risks, and operating readiness, then define a prioritized path from experiments to scalable cloud delivery.
Focused assessment and prioritized roadmap
Custom AI Agents
Build task-focused agents that use approved knowledge, call business tools, follow guardrails, and hand work to people when judgment is required.
Pilot through production deployment
AI Voice Agent
Deploy natural voice agents for inbound calls, qualification, booking, support, dispatch, and structured follow-up with reliable human handoff.
Conversation design, integration, pilot, and monitored rollout
Document AI
Classify documents and extract validated, structured information from invoices, forms, reports, images, and industry-specific records.
Extraction proof of value through production workflow
Enterprise Knowledge Base & RAG
Turn governed company content into permission-aware search and grounded agent answers with citations, freshness controls, and measurable retrieval quality.
Knowledge readiness, retrieval pilot, and governed rollout
AI Evaluation & Observability
Measure groundedness, task completion, tool use, safety, latency, and cost before release, then trace and monitor quality in production.
Evaluation baseline, release gates, tracing, and operating cadence
Data services
Data & Integration
Create the reliable pipelines, contracts, semantic models, APIs, and business-system connections that AI and analytics depend on.
Microsoft Fabric + AI
Build governed OneLake, lakehouse, warehouse, semantic-model, and Fabric data-agent foundations that make analytics and AI easier to use securely.
Architecture, implementation, migration, and enablement
ELT & Data Integration
Connect SaaS, databases, files, and APIs through reliable batch, event, CDC, ETL, or ELT pipelines with testing, lineage, and recoverability.
Source assessment through monitored production pipelines
RevOps Integration
Connect marketing, CRM, quoting, scheduling, billing, support, and analytics so revenue teams share clean lifecycle data and dependable handoffs.
Lifecycle mapping, integration, automation, and operating ownership
Cloud & Data Architecture Review
Review cloud platforms, data pipelines, quality, security, cost drivers, and operating risks, then define a practical modernization roadmap.
Architecture assessment and modernization roadmap
Cloud AI platforms
Cloud AI Platforms
Modernize and deliver AI on Microsoft Azure, AWS, or Google Cloud using the platform’s native identity, data, agent, document, evaluation, and operating services.
AI Modernization on Microsoft Azure
Modernize AI, automation, and knowledge workflows on Microsoft Azure with Foundry agents, Azure AI Search, Document Intelligence, Fabric, Copilot Studio, and production controls.
Use-case assessment, target architecture, pilot, migration, and production rollout
AI Modernization on AWS
Modernize AI and automation on AWS with Amazon Bedrock agents and knowledge bases, Textract, serverless workflows, secure data foundations, and production observability.
Use-case assessment, target architecture, pilot, migration, and production rollout
AI Modernization on Google Cloud
Modernize AI and automation on Google Cloud with Vertex AI, Agent Development Kit, Agent Engine, Gemini, Document AI, BigQuery, and production-ready serverless delivery.
Use-case assessment, target architecture, pilot, migration, and production rollout
Document AI on Microsoft Azure
Build production document workflows on Azure with Document Intelligence, Microsoft Foundry agents, AI Search, Fabric, APIs, confidence controls, and human review.
Document assessment, extraction benchmark, workflow pilot, and production rollout
Document AI on AWS
Build production document workflows on AWS with Amazon Textract, Bedrock, S3, Lambda, Step Functions, validation rules, human review, and monitoring.
Document assessment, extraction benchmark, workflow pilot, and production rollout
Document AI on Google Cloud
Build production document workflows on Google Cloud with Document AI processors, Gemini, Vertex AI, Cloud Storage, BigQuery, Workflows, validation, and human review.
Document assessment, processor benchmark, workflow pilot, and production rollout
Managed operations
Managed Operations
Keep production AI and automation measurable, supportable, cost-aware, and owned as models, data, integrations, and policies change.