FAQ

The hard questions, answered straight.

Three areas where buyers usually press hardest: how we keep your data safe, how we embed in your environment, and how we prove the AI investment is paying off.

Section 01 — Security

Built to your security posture.

Enterprise-grade security is the floor, not the ceiling. Here's how we work inside your existing controls, contracts, and compliance regime.

  • Inside your tenant. We build in your approved Microsoft, Google, Salesforce, Workday, ServiceNow, or internal environment. Your data does not leave your boundary, and we operate under your governance, retention, and access policies — not ours.
  • We use the model providers your security team has already approved — typically Azure OpenAI, AWS Bedrock, Google Vertex, or an on-prem model your enterprise has cleared. Prompts and outputs follow your existing data-processing agreements. We don't introduce new third-party model providers without your explicit sign-off.
  • We design for least-privilege access from day one: role-based controls, data minimization in prompts, redaction where required, and full audit logging. For regulated workloads (HIPAA, GDPR, SOC 2, FINRA, etc.) we align to your existing controls and document everything for your auditors.
  • Yes. We work within your standard contracting and security review process — including vendor risk assessments, SIG questionnaires, penetration test attestations, and any custom DPAs. We've been through enterprise procurement before; we don't slow it down.
  • You do. Everything we build inside your environment — code, prompts, configs, documentation — belongs to you. We hand it over so your internal teams can maintain, evolve, and extend it without us.
  • We treat AI tools like any other production software. That means input validation, output guardrails, retrieval grounding for factual workloads, human-in-the-loop checkpoints for high-stakes decisions, monitoring for drift and abuse, and a documented incident response path. Safety isn't a feature we add at the end — it's part of the build.

Section 02 — Embedding

Inside your stack. Not beside it.

We don't ship another tab. We build where the work already happens — inside the platforms your IT, security, and data teams already trust.

  • We don't ship a separate app you have to log into. We build inside the systems your people already use — Teams, Outlook, Salesforce, ServiceNow, SharePoint, your data warehouse, your internal portal. The tool shows up where the work already happens, so adoption isn't a fight.
  • With them, always. We engage IT, security, data, and platform teams from day one — before we write code. We follow your change-management process, your release pipelines, and your environment promotion rules. We don't ship shadow IT.
  • Microsoft 365 and Azure (Copilot Studio, Power Platform, Azure OpenAI), Google Workspace and Vertex AI, Salesforce (Agentforce, Einstein, Flows), ServiceNow, Workday, SharePoint, Snowflake, Databricks, and a long tail of internal apps via APIs. If your team has standardized on it, we can build inside it.
  • Yes. Tools we build authenticate through your SSO (Okta, Entra ID, Ping, etc.), respect your existing entitlements, and read from your governed data sources (Snowflake, BigQuery, Databricks, Redshift, on-prem warehouses). We don't duplicate data or create new systems of record.
  • You keep everything: the code, the documentation, the runbooks, and the trained internal team. We design for handoff from the first sprint — pairing with your engineers, writing maintenance docs as we build, and walking your team through every component before we step back.

Section 03 — Measuring Value

Measurable value, not vibes.

Every build ships instrumented. We define the success metrics with you up front, then track them — adoption, hours, decisions, outcomes.

  • We define success metrics with you before we build, not after. Typical metrics fall into four buckets: adoption (active users, frequency, depth of use), time returned (hours saved per user per week), decision quality (accuracy, cycle time, error rate), and business outcome (revenue, cost, retention, throughput). We instrument for these from day one.
  • First builds typically ship in 4–8 weeks, with adoption and value signals visible within the first 30 days of launch. Most teams have a defensible value story to share with leadership inside the first quarter. We'd rather show you a small, real number than a large, hypothetical one.
  • MIT's 2025 research found only 5% of AI pilots reach production. We design every engagement to land in that 5%: scoped to a real workflow with named users, built inside the production environment from day one, instrumented for adoption, and handed off to an internal owner. No standalone demos. No orphaned prototypes.
  • We'd rather find that out in week three than month nine. If a build isn't earning its keep, we'll tell you — and we'll either reshape it, redirect to a higher-leverage opportunity, or recommend you stop. Sunk-cost is the enemy of good AI investment.
  • Yes. Many engagements start with instrumenting and evaluating an existing tool — internal build, vendor product, or copilot rollout — to surface adoption gaps and value leakage. Sometimes the highest-leverage move is fixing what's already shipped.
  • MIT's research found AI initiatives built with external partners succeed at roughly twice the rate of internal-only efforts. The gap isn't about talent — it's about adoption discipline, outside perspective on friction, and pattern-matching across many builds. That's the value we bring on top of your team.

Still have questions?

Bring the hardest one to a 30-minute working session.

Security review, integration scoping, value attribution — whatever's blocking the next step, we'll work through it on a call.

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Most engagements start with one team and one high-friction workflow. We prove the pattern, then scale it.

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