Turingly helps enterprise leaders decide where AI should create value, build the business case, and turn it into production workflows that compound.
Competitive advantage will come from companies building their own software and models. I wrote that in 2023. Three years later, it's playing out. Turingly exists to help enterprises actually do it.
A standalone assistant is useful. A workflow embedded into revenue, service, risk, finance, or operations is worth funding.
Models keep getting cheaper and stronger. The bottleneck is which workflow should change, who owns the outcome, and how success is measured.
Production AI spans models, data, orchestration, agents, evaluation, governance, and security, running on Salesforce, ServiceNow, Workday, Snowflake, Databricks, and the hyperscalers. The job is to evaluate the architecture, challenge the assumptions, and choose what to build, buy, or defer, then tie it back to the outcome that justified it.
Structured pilots with clear go/no-go criteria, then a graduation path from proof-of-concept to enterprise deployment. We map, design, build, test, and measure. ClarityHoop is one example.
The best deployments help people make better decisions, communicate more clearly, resolve issues faster, and execute with less friction.
Most AI work fails in the gaps between strategy, engineering, and the enterprise. Turingly was founded by Sanej Bandgar after two decades working all three.
Fifteen years translating emerging AI into business cases, roadmaps, and funded programs across energy, financial services, industrials, healthcare, and the public sector. Deciding what to pursue, defer, or ignore, then moving it from pilot to production with measurable outcomes.
Built large-scale enterprise platforms across analytics marketplaces, reliability workflows, and front, mid, and back-office systems, with earlier years in software R&D on unstructured-data platforms. That discipline now sits behind ClarityHoop and the agent-workflow patterns shipped under Turingly.
Builder and buyer of enterprise AI platforms, and the translator between the technical frontier and the executive table. AI does not fail because of models. It fails because of ownership, procurement, data, integration, and adoption.
We'll respond within 48 hours if there's a fit.
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