GTM intelligence tools that change how infrastructure is bought, and production AI systems built to run in regulated environments. Live and accepting demos — reach out if you want to know what’s in the pipeline.
A 15-minute self-service assessment that sizes a workload against a GPU catalog, benchmarks the vendor’s rates against the competitive landscape, and scores the prospect’s fit — so the sales team’s first conversation starts from a qualified position rather than discovery.
See how it works →Given a workload and a target GPU family, produces the compute blueprint for an on-premises AI factory deployment — server configuration, cluster topology, cooling class, and serving runtime, derived from physics rather than estimation. Designed to map to OEM-validated stacks; covers the full infrastructure scope with compute as the primary output in the current release.
Request a demo →A structured assessment across six domains — data architecture, storage, engineering pipelines, infrastructure, compliance, and cost — that determines whether an organization’s data foundation can support the AI workloads being planned. Produces a scored gap report before the pilot begins.
Runs real inference load against hardware a team is evaluating and surfaces the measurements that tend to shape the purchase decision — quantization tradeoffs, context window limits, sustained load behavior, and concurrency ceiling. The team comes away with a clearer sense of what a given device can handle before they commit.
Request a demo →Validates a data center facility against a planned GPU cluster before the hardware order is placed. Checks power, cooling, floor load, rack space, aisle containment, and network fabric readiness — and surfaces structured findings per domain. Identifies what needs remediation before the cluster ships, so the hardware order and facility work can proceed on parallel tracks.
Request a demo →Applies a published methodology to a buyer’s workload, model, and hardware configuration to produce a Executive Bridge Report: cost-per-million-tokens as a range, utilization sensitivity curve, KV cache memory tax, and three-year TCO projection. Every output traces to a vendor-published specification or documented assumption — built to survive a finance committee, not just an engineering review.
Request a demo →Automates the full KYC due diligence and onboarding sequence for financial institutions — entity data, credit ratings, sanctions screening, and PEP checks run in the correct order, risk evaluated by deterministic logic, compliance documentation generated automatically. Low and medium risk cases clear without analyst involvement; the analyst queue receives only cases that require human judgment, pre-populated with all findings.
Request a demo →A shared engine — LangGraph graph controls all routing, LLM handles retrieval synthesis — with five independently licensable products: Financial Intelligence, Compliance Intelligence, Legal Intelligence, Clinical Intelligence, and Manufacturing Intelligence. Each SKU ships with its own corpus scaffold, domain rules, and documentation as a self-contained deployment.
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