AI Consulting in Research Triangle

Part 11 compliance is an architectural decision, not a checkbox applied after the fact. When we scope a build for a biotech or pharma team in the Triangle, we map every record that the system creates, modifies, or transmits against Part 11's requirements for audit trails, access controls, and electronic signature linkage before we write the first line of integration code. That means the build produces an audit trail that logs the operator identity, timestamp, and action for every record event — not as a separate logging layer bolted on, but as part of the core data model. For electronic signatures specifically, we scope against the hybrid approach most validated systems use: Part 11 Subpart C compliant signatures where required, with signature manifestos that bind the signer identity, the record, and the signature meaning in a single artifact. We don't certify your system — that's your validation team's job, and your CSV protocol owns the IQ/OQ/PQ. What we deliver is a build designed to pass validation review, with the technical documentation your validation team needs to execute the protocol without guessing what the system does.

Yes, and this is a workflow class we know specifically. Sponsored research at a major academic medical center sits at the intersection of three systems that rarely talk cleanly to each other: the institutional research administration platform (Coeus, Cayuse, or Kuali Research are the common ones at Triangle institutions), the NIH eRA Commons for federal reporting, and whatever the department uses internally to track effort and expenditures — often a combination of spreadsheets and PeopleSoft queries run by a grants manager. The friction shows up at specific points: post-award setup when an award arrives in eRA Commons and someone has to manually re-enter project data into the internal system, effort reporting periods when certifications have to be collected from faculty across multiple departments, and closeout when the final FFR and invention disclosure both need to happen inside a compressed window.

We don't provide legal advice on IP ownership — that question lives with your tech transfer office and outside IP counsel. What we do is build systems that don't create new IP ambiguity. When we work with a Triangle spinout that originated from Duke or UNC research, we scope the build to use your company's data and your commercial workflows only, with no data path that runs through institutional systems or accesses institutional records. If the spinout has a sponsored research agreement still active with the parent institution, we document the data flows explicitly so your tech transfer office can confirm the build doesn't touch anything in the SRA scope. For companies at the early spinout stage, the $99 audit is useful for a different reason than it is for established firms: it produces a written map of what data the company owns and controls, what data still lives in institutional systems, and where the handoff points are. That map is useful to share with the TTO before you build anything that automates across that boundary.

The entry point is the same regardless of industry: the $99 AI readiness audit produces a written assessment of your highest-leverage automation targets, the data infrastructure you have to work with, and any compliance constraints that shape what's buildable. From there, fixed-price builds run $2,500 to $15,000 depending on integration complexity — a single-system workflow automation at the low end, a multi-system build touching a regulated data store, a CRO partner integration, and a validated output layer at the high end. Biotech and pharma builds do run toward the higher end of that range because the validation documentation, the audit trail architecture, and the compliance review cycle add real engineering time that a standard business automation build doesn't carry. That's not a premium for the industry label — it's the actual work. RTP tech teams scaling fast tend to land in the middle of the range: knowledge base builds and onboarding automation are less compliance-heavy but often touch more internal systems than they initially appear to.

Both, and the cross-organizational nature of the relationship matters for how we scope the build. When a Triangle-based sponsor brings us in to streamline a workflow that involves a CRO or contract lab, we map the full data flow first — what the sponsor owns and controls, what the CRO produces and transmits, and where the handoff creates manual work. The most common friction point is the data package handoff: a CRO delivers a dataset that doesn't match the sponsor's internal naming conventions or folder structure, and someone at the sponsor does a manual normalization before the data goes into the analysis pipeline. That's a well-defined automation problem. What makes it more complex than a standard integration is that the CRO's data transmission system is usually outside the sponsor's control — we're building on the receiving end, not the sending end, so the build has to be flexible enough to handle the CRO's format variations without breaking. We've also worked directly with CROs that want to improve their own delivery workflows — faster QC cycles, automated deviation flagging before the package goes to the sponsor, cleaner audit trail on the lab side. Both entry points work.