Enterprise AI · Technology · Talent
Enterprise AI, from pilot to production.
Strategy, engineering, and scarce AI talent under one roof — so the AI you have proven becomes the AI you run.
Intelligence, engineered.Frisco · Dallas · Detroit · Atlanta · Dubai · Kuala Lumpur · Pune
A decade of delivery for
TTS
TFS
01 — Point of view
Most enterprise AI never leaves the pilot. Not for lack of models — for lack of ready data, early governance, real integration, and the engineers to do the work. We close that gap.
01The data isn’t readyPilots run on curated extracts. Production runs on live, owned, governed data.
02Governance arrives lateRisk review at the end becomes a veto. Built in from intake, it becomes a release path.
03It sits beside the workValue is created inside ERP, CRM, and ITSM workflows — not in a separate chat window.
04The team isn’t thereThe engineers who can do this are scarce. We bring them, vetted, in weeks.
02 — Capabilities
Four practices. One accountable partner.
Engage a single practice or all four. Strategy, engineering, and talent share one plan, one team, and one measure of success.
01
Artificial IntelligenceStrategy, agents, and platforms that hold up in production.
02
TechnologyThe modern foundations AI depends on.
03
TalentScarce AI and engineering capacity, deployed in weeks.
04
ConsultingOperating models that make transformation stick.
03 — Agentic AI spotlight
Agents that work inside the enterprise, not beside it.
We design agent systems that read from and act on your systems of record — with explicit checkpoints where a person approves, and a governance layer that sees every step.
Agentic AI & Automation →04 — Engagement model
One path from first assessment to scaled operation.
Engage us as a delivery partner, a talent partner, or both. Enter at any stage; every stage ends with something you keep.
01AssessAI readiness assessment and a prioritized value case
02ArchitectReference architecture, data plan, and governance design
03BuildProduction-grade agents, applications, and platforms
04StaffVetted AI and engineering talent or managed delivery pods
05ScaleOperating model, AI Center of Excellence, and run support
05 — Industries
Context earned in the industries we serve.
06 — Selected work
In production, not in a deck.
Automotive & MobilityCase study
A global automotive OEMCHALLENGEWarranty claims were triaged by hand across dozens of dealer and quality systems, delaying root-cause detection.
APPROACHAn agentic claims-triage workflow on Azure and Databricks, with human review on high-value claims.
40%faster claim resolution
Financial ServicesCase study
A top-10 US bankCHALLENGEGenerative AI pilots were blocked in model-risk review with no path to production.
APPROACHA governance operating model, an LLMOps platform, and a managed delivery pod to run it.
12use cases in production in 9 months
Aerospace & DefenseCase study
A Fortune 500 aerospace manufacturerCHALLENGEMaintenance knowledge was locked in decades of manuals and service records.
APPROACHA retrieval-based assistant for MRO technicians on a secure cloud tenant, built and staffed by a dedicated pod.
35%reduction in time-to-diagnosis
07 — By the numbers
10+Years of enterprise IT delivery
500+Engineers deployed
40+Enterprise clients
7Offices across 4 countries
08 — Global presence
Seven offices. Four countries. One delivery model.
OnshoreFrisco · Dallas · Detroit · AtlantaClient-side leadership, architecture, and regulated work, close to the business.
NearshoreDubai · Kuala LumpurRegional hubs that keep delivery within a few time zones of Middle East and APAC clients.
OffshorePuneOur global delivery center for engineering scale, managed pods, and follow-the-sun support.
09 — Ecosystem
Platform-fluent. Partner-ready.
Platforms we build on
AWS
Microsoft Azure
Google Cloud
Salesforce
Snowflake
Databricks
Oracle
OpenAI
Anthropic Claude
Microsoft Copilot
AWSMicrosoft AzureGoogle CloudSalesforceSnowflakeDatabricksOracleOpenAIAnthropic ClaudeMicrosoft CopilotDelivery & alliance partners
10 — Talent engine
Scarce AI talent, vetted for the work — not the keyword.
01SourceA global bench across four countries, built over a decade of enterprise delivery.
AI / ML, data, cloud, and platform engineersTechnical and executive leadership searchReferral network of alumni engineers
02VetSpecialized AI and ML vetting, reviewed by practicing engineers — not keyword screens.
Hands-on build exerciseSystem design and evaluation reviewMLOps, security, and data depth
03DeploySpeed to deploy, as individuals or as managed delivery pods with a single point of accountability.
Shortlists in days, not weeksOnshore, nearshore, or offshoreOngoing performance reviews
11 — Latest insights
Perspectives from the work.
Agentic AI14 Sep 2026
Why AI agents stall after the pilot — and what production actually requiresMost agent pilots succeed on a narrow demo and fail on the fourth integration. The gap is rarely the model; it is identity, observability, and the cost of exceptions.Principal, Agentic AI Practice · 9 minGovernance21 Aug 2026
Designing an AI governance operating model that does not slow deliveryGovernance that arrives at the end of a project becomes a veto. Built into intake, evaluation, and release, it becomes a release mechanism.Director, AI Risk & Governance · 11 minTalent30 Jul 2026
The AI engineering talent gap is a systems problemHiring more ML engineers does not fix a team with no data engineers, no platform owners, and no evaluation discipline. Plan for the whole system.Head of AI & Engineering Talent · 7 minData & Platforms18 Jun 2026
Data readiness for agentic AI: six questions to answer firstBefore an agent can act on enterprise data, someone has to know who owns it, how fresh it is, and what the agent is allowed to change.Principal Architect, Data & AI Platforms · 8 minModernization09 Apr 2026
Legacy modernization is the AI strategy nobody budgets forAI programs inherit the constraints of the systems beneath them. Modernizing the right three integrations often matters more than choosing the right model.Partner, Technology Modernization · 10 minStrategy03 Dec 2025
Measuring AI ROI in the enterprise: from activity metrics to the P&LPrompts served and hours saved are not returns. A practical framework for tying AI programs to cost, revenue, and risk outcomes the CFO recognizes.Partner, Transformation Advisory · 12 min12 — Responsible AI
Trust is an engineering requirement.
GovernanceEvery system ships with an owner, a risk tier, and an evaluation plan before it reaches users.
SecurityLeast-privilege access for agents, isolated environments, and no client data used to train third-party models.
Human oversightConsequential actions route through a named person. Automation earns autonomy through measured performance.
Data privacyData stays in the client’s tenant and jurisdiction, with retention and deletion defined up front.
13 — Start a conversation
Let’s move your AI into production.
Tell us where you are — a stalled pilot, a platform decision, a team to build. A practice lead will reply within one business day.