AI Proof of Concept Validate Before You Invest
Petronella Technology Group runs a 3-stage AI engagement methodology built for regulated organizations: Assess, Prototype on our private datacenter cluster in Raleigh, and ship a Production Hardware Blueprint sized to your real workload. Your data, code, and models stay on a private cluster the entire time. Call (919) 348-4912.
In short
- Most enterprise AI demos die in the gap between "it worked once" and "it scales in production." A scattered demo on a laptop never tells you what production will cost or how it will break.
- Our 3-phase methodology - Assess, Prototype, Blueprint - finds the bottleneck before you fund production. Compute saturation, GPU memory ceiling, data pipeline I/O, inference latency, integration friction, observability gaps. We hunt them in Stage 2.
- We build prototypes on our own datacenter AI cluster in Raleigh, NC, so your data, code, and models never leave a private environment. We sign NDAs, BAAs for HIPAA, and operate inside CMMC-aligned enclaves where the workload requires it.
- You leave with a working artifact, telemetry, and a hardware blueprint sized to your real load, not a slide deck. The blueprint specifies servers, GPUs, storage, network, deployment topology, an operations runbook, and a 1-year and 3-year total cost of ownership model.
Most AI POCs Die Quietly. Here Is How.
Enterprise AI initiatives have a graveyard. The pattern is consistent enough that you can spot it before you fund the next one.
The four common failure modes
- Laptop demo with cherry-picked data. The prototype works on a curated 200-row sample. Production data has 200 million rows, half of them dirty, and the model collapses on edge cases nobody fed it during the demo.
- Wrong cloud, wrong data class. The team picks a public AI API for the prototype, then discovers the production data is HIPAA-regulated, CMMC-controlled, or trade-secret sensitive. The prototype cannot be moved to production without rebuilding from scratch.
- No telemetry, no sizing. The prototype runs on a developer laptop with one user. Nobody measured tokens per second, GPU memory pressure, or p99 latency. Production hardware sizing becomes a guess. Capacity planning becomes a prayer.
- Integration gaps surface at production time. Auth, rate limits, write-back into the system of record, legacy schema mismatches. None of it touched the prototype. All of it shows up the day production goes live.
Why our methodology corrects each one
- Stage 1 Assess scopes the data class, regulatory framework, integration targets, and success criteria before anyone touches a model. Wrong-cloud problems and wrong-data-class problems get caught here.
- Stage 2 Prototype runs on real or representative production data, against realistic concurrency, integrated to real upstream and downstream systems, with full telemetry. Demo problems die here.
- Stage 2 telemetry measures token throughput, GPU memory pressure, KV-cache behavior, retrieval round-trip times, and request shape distribution. Sizing becomes a calculation, not a guess.
- Stage 3 Blueprint ships a production-ready hardware specification with operations runbook and TCO. Integration paths, deployment topology, regulated enclave requirements, and capacity headroom are documented before procurement.
Assess - The AI Readiness Diagnostic
Stage 1 is a structured discovery engagement. Before any code is written or any GPU is provisioned, we sit with your engineering and data leaders to map what you actually have, what you actually need, and whether the project deserves the investment of a Stage 2 prototype. Most engagements that fail in production were broken in Stage 1, where nobody asked the hard questions.
What we deliver
- Written readiness report covering current data state, integration map, regulatory scoping, and success-criteria definition.
- Data and integration map identifying upstream sources, downstream targets, latency budgets, and any legacy-schema friction we will hit during prototyping.
- Regulatory scoping for HIPAA, CMMC L2 or L3, NIST 800-171, NIST 800-172, SOC 2, and any vertical-specific framework that applies to your data class.
- Success-criteria definition in plain language and in measurable thresholds. Latency targets, accuracy targets, throughput targets, cost ceilings, and acceptable failure modes.
- One-page go or no-go recommendation with the specific reasons we would or would not proceed to a Stage 2 prototype.
- Stage 2 scope proposal if we recommend proceeding. Concrete week count, deliverable list, milestone schedule, and engagement cost.
How it runs
A 30-minute discovery call kicks off the diagnostic. From there, we run 1 to 2 working sessions with your engineering, data, and security leaders, review a representative data sample (or schema and description), and produce the written report. Total elapsed time from kickoff to delivered report is typically 5 to 10 business days, gated by stakeholder availability and how quickly your team can share the data sample. The clock does not start until we have what we need from you.
What you provide
A point of contact, a 30-minute discovery call, 1 to 2 working sessions with engineering and data leadership, a representative data sample (or a schema and a description if the data itself cannot leave your environment yet), and a clear statement of the business outcome you are trying to reach. We sign a mutual NDA before any sample data changes hands. If your workload requires HIPAA, we sign a BAA at this stage as well.
The outcome
A written diagnostic plus a one-page go or no-go. If we recommend proceeding, you get a Stage 2 scope proposal in the same delivery. If we recommend stopping, the diagnostic itself is the deliverable, and we will tell you exactly which assumptions broke and what would have to change before the project should be retried. We would rather lose a Stage 2 engagement than ship a prototype that misleads your investment committee.
Book the AI Readiness Diagnostic →
How long does the AI Readiness Diagnostic take?
The discovery call itself is 30 minutes. From kickoff to written report typically takes 5 to 10 business days, depending on stakeholder availability and how quickly we can review your data sample. We do not start the clock until we have what we need from your team, so a slow week on your side does not eat into our delivery window.
What do we need to provide for the diagnostic?
A point of contact, a 30-minute discovery call, 1 to 2 working sessions with engineering and data leadership, a representative data sample (or a schema and a description), and a clear statement of the business outcome you are trying to reach. We sign a mutual NDA before any sample data changes hands. For regulated data, we sign a BAA or a CMMC-aligned engagement letter as well.
Prototype - On the Datacenter AI Cluster
Stage 2 is where the methodology earns its keep. We build a working prototype against your real data on our private datacenter AI cluster in Raleigh, North Carolina. The prototype runs at realistic concurrency, with realistic data volume, integrated to real upstream and downstream systems. We hunt the six bottlenecks that kill production AI deployments. You see the work as it happens, week by week, in scheduled working sessions and read-only dashboards.
The cluster, the privacy posture, and what does not happen
Your data stays on the Petronella cluster for the duration of the prototype. Nothing is sent to a third-party AI API. Nothing is used to train any external model. Access is restricted to the named engineers on your engagement under a signed NDA. If your workload requires HIPAA, we sign a Business Associate Agreement and operate the prototype inside an environment configured to the HIPAA Security Rule. If your workload requires CMMC, we run the prototype inside an enclave aligned with the framework level you operate under, whether that is L2 mapped to NIST 800-171 or L3 mapped to NIST 800-172. We do not deploy regulated workloads to public AI APIs at any stage.
What we actually build
- A working proof against your real data, or a representative sample large enough to surface production-class behavior.
- Realistic load profile - the concurrency, request shape, and data volume that mirror your projected production environment.
- Integration to upstream and downstream systems - databases, identity providers, ticketing, CRM, ERP, and the systems of record that the AI workflow has to read from and write to.
- Telemetry from token-level to request-level so we can size production with calculation, not guesswork.
- Reproducible benchmarks against your data and your concurrency profile, so we can re-run them under different hardware assumptions.
The 6 bottlenecks we hunt during a prototype
This is the meat of the methodology. Production AI deployments fail at predictable points. We instrument the prototype to expose each one before it becomes an expensive surprise.
1Compute saturation
GPU utilization, queue depth, and batch-size effects under your real concurrency. We measure where the GPU starts queuing requests, what batch size optimizes throughput vs latency, and how scaling out compares to scaling up. Without this data, your production sizing is a guess. With it, you can put a number on every additional dollar of GPU spend.
2GPU memory ceiling
The mismatch between context window, model size, and available VRAM is one of the most common production failures. We measure KV-cache pressure, concurrent-request memory footprint, and the concurrency cap your hardware tier will support. The blueprint is sized to your real ceiling, not a benchmark headline number.
3Data pipeline I/O
Vector store query latency, embedding regeneration cost, retrieval-augmented generation round-trips, and document chunking strategy. This is where most enterprise AI deployments quietly bleed performance. We benchmark each layer of the pipeline so the blueprint specifies storage and network capacity sized to actual retrieval load.
4Inference latency under concurrency
Median latency is a vanity metric. Tail latency is the truth. We measure p50 versus p99 under realistic concurrency, identify retry storms before they hit production, and document the latency profile under burst load. The blueprint includes the headroom needed to keep p99 inside your SLO when traffic spikes.
5Integration friction
Authentication flow, third-party rate limits, ticket and record write-back, legacy schema mismatches. The places where the AI workflow has to talk to your existing systems are where most prototypes that look great in isolation fall apart at production scale. We exercise every integration path during the prototype.
6Observability gaps
No token accounting, no per-request audit trail, no failure-class taxonomy. Without observability, production debugging takes hours per incident. The blueprint specifies the telemetry stack, log retention policy, and audit posture that satisfies your regulatory and operational requirements out of the gate.
What you see during the prototype
Stage 2 is collaborative, not a black box. You get weekly working sessions with our engineers, read-only dashboards into the cluster, reproducible benchmarks against your data, recorded test runs, and written status notes. Your stakeholders see the prototype evolve in real time. When an unexpected bottleneck shows up, you hear about it the day it shows up, not at the final readout. This is the posture every enterprise customer wants and that very few AI consultancies actually deliver.
Typical engagement length
Most enterprise prototypes run 4 to 10 weeks. The width of the range is set by data complexity, integration count, and whether the production workload requires a regulated enclave from day one. We give you a concrete week count and a milestone schedule before Stage 2 starts. If a milestone slips, you hear about it the week it slips, with the reason and the corrective plan.
Do you keep our data on your cluster, or move it?
Your data stays on our private datacenter cluster in Raleigh for the duration of the prototype. Nothing is sent to a third-party AI API, nothing is used to train any external model, and access is restricted to the named engineers on your engagement under a signed NDA. If your workload requires HIPAA, we sign a BAA. If it requires CMMC, we operate the prototype inside a CMMC-aligned enclave.
What if the prototype shows the project should not proceed?
That is a legitimate outcome and we say so plainly. If the bottlenecks we find are unfixable within reasonable scope, or the business case does not survive the telemetry, the Stage 2 deliverable will recommend stopping. You leave with the data and the report. We would rather lose a Stage 3 engagement than deliver a hardware blueprint that ships you into a production failure.
How long is a typical enterprise AI prototype?
Most enterprise prototypes run 4 to 10 weeks. The width of the range is set by data complexity, integration count, and whether the production workload requires a regulated enclave from day one. We give you a concrete week count and a milestone schedule before Stage 2 starts.
Blueprint - Production Hardware Specification
Stage 3 translates Stage 2 telemetry into a production-ready specification. The blueprint is the document your CFO, your CIO, your security officer, and your hardware procurement lead can sign off on without filling in blanks themselves. It is sized to the load you actually measured, not the load somebody guessed at, and it is specified at the lowest hardware tier that meets your latency and concurrency targets with documented headroom.
Sizing methodology
We start with the prototype telemetry: concurrency, p50 and p99 latency under realistic load, GPU memory pressure, KV-cache behavior, retrieval round-trip times, and request shape distribution. We then model production load by combining your projected user count, peak versus steady-state ratio, expected data growth curve, and the latency targets you set in Stage 1. The blueprint specifies hardware that meets the targets with a documented headroom buffer (typically 30 to 50 percent depending on workload class). We do not over-spec. We do not under-spec.
What the blueprint document covers
- Server count and form factor. Rack-mount versus tower versus blade, U-count, density per rack, redundancy posture.
- CPU specification. Core count, clock target, NUMA topology, and the workload reasoning that drove the decision.
- RAM specification. Capacity per node, channel configuration, and headroom for KV-cache growth and concurrent sessions.
- GPU specification. Per-server GPU count, model class, memory per GPU, NVLink or PCIe topology, and the bottleneck taxonomy from Stage 2 that informed the choice.
- Storage specification. NVMe versus SAS, capacity per node, vector store sizing, hot versus cold tier, snapshot and backup posture.
- Network specification. Throughput per link (10, 25, or 100 Gigabit Ethernet), east-west versus north-south traffic, segmentation for regulated workloads.
- Deployment topology. On-premises in your datacenter, colocation in a Petronella-supported facility, hybrid, or regulated-cloud. Each option includes the trade-offs in cost, latency, regulatory posture, and operational burden.
- Operations runbook. Monitoring stack, patching cadence, model lifecycle management, capacity review cadence, incident response runbook, and the on-call posture appropriate to your environment.
- Total cost of ownership model. 1-year and 3-year, capex versus opex trade, with the assumptions documented so your finance team can stress-test the model with their own variables.
Hardware cluster cross-references
The blueprint typically references one or more of our existing hardware specifications. Production deployments built on the Petronella methodology often draw from these reference platforms:
- AI workstations sized for production training and inference workloads - rack and tower configurations for development, fine-tuning, and edge inference.
- NVIDIA DGX system reference architectures - sourced via the NVIDIA Elite Partner Channel for high-density training clusters.
- Custom AI server builds when the workload pattern does not map cleanly to a stock platform.
- Private AI solutions covering the deployment posture, cluster software stack, and managed-operations tier we layer on top of the hardware.
- GPU server hosting when colocation in a Petronella-supported facility is the right deployment topology for your workload.
Deliverable format
The blueprint ships as a written specification document plus a structured bill of materials, a deployment-topology diagram, an operations runbook, and the TCO model in a format your finance team can ingest. It is the document your hardware procurement lead, your security officer, and your CIO can sign without filling in blanks themselves.
How do you decide hardware sizing for production?
We extrapolate from the Stage 2 prototype telemetry. We measure concurrency, p50 and p99 latency, GPU memory pressure, KV-cache behavior, retrieval round-trip times, and request shape distribution. We then model production load with your projected user count, peak versus steady-state ratio, and data growth. The blueprint specifies hardware at the lowest tier that meets your latency and concurrency targets with a documented headroom buffer. We do not over-spec. We do not under-spec.
Can the blueprint be deployed in a regulated environment (HIPAA, CMMC L2 or L3)?
Yes. Our team holds CMMC-RP credentials, Petronella Technology Group is a CMMC-AB Registered Provider Organization (RPO #1449), and we hold a BBB A+ rating since 2003. Regulated blueprints specify on-premises or colocation hardware inside an enclave aligned to your applicable framework: NIST 800-171 for CMMC L2, NIST 800-172 for CMMC L3, HIPAA Security Rule plus signed BAA for ePHI workloads. We do not deploy regulated workloads to public AI APIs.
Built for Regulated Workloads From Day One
Most AI consultancies are software firms that learned compliance after the fact. Petronella Technology Group is a cybersecurity and compliance firm that built an AI practice on top of 20+ years of regulated-environment experience. Every prototype runs inside the same security posture we apply to a CMMC engagement, a HIPAA assessment, or a digital forensics investigation.
What this means in practice: when an engagement requires a Business Associate Agreement, we already have the legal and operational infrastructure to sign one. When it requires a CMMC enclave, we have the people and the process. When it requires evidence-handling discipline, we have been doing it for two decades. The AI practice rides on top of that foundation, not the other way around.
Where the Methodology Earns Its Keep
The 3-stage methodology is built for verticals where AI prototype mistakes are expensive and the data class will not tolerate a public AI API. These are the verticals where we do most of our prototyping work.
Engineering firms
AEC firms (architecture, engineering, construction), civil and structural engineers, MEP designers, and the design-simulation workloads that come with them. Your CAD models, simulation data, and proprietary workflows are some of the most IP-sensitive assets a firm owns. The prototype runs on a private cluster, never on a public AI API. Engineering firms in our region are a priority practice for us.
Engineering firms practice →Healthcare
Health systems, specialty practices, and digital-health platforms operating under HIPAA and the HIPAA Security Rule. Electronic protected health information cannot leave a Business-Associate-covered environment. Our prototypes run inside a HIPAA-aligned posture with a signed BAA before any data sample changes hands. Provider workflows, claims data, and clinical documents all stay on our private cluster.
Healthcare practice → Healthcare AI consulting →Defense and CMMC contractors
Defense industrial base contractors, aerospace suppliers, and any organization that handles Controlled Unclassified Information (CUI) under DFARS 252.204-7012 or the CMMC framework. Our prototypes run inside enclaves aligned with NIST 800-171 for CMMC Level 2 and NIST 800-172 for CMMC Level 3. We consult across all three CMMC levels (L1, L2, and L3).
CMMC compliance → NIST 800-171 →Legal
Law firms, in-house legal departments, and litigation support practices that handle privileged client work-product, discovery materials, and confidential transactional documents. The data class does not tolerate a public AI API. Our prototypes run on a private cluster with audit trails the firm's information governance committee can sign off on.
Legal industry deployment →Healthcare cybersecurity
The intersection of clinical workflows and cybersecurity is where most AI prototypes for healthcare actually live: anomaly detection on clinical access logs, ePHI exposure scanning, and AI-augmented incident response inside HIPAA-covered environments. We treat the prototype as a security engagement first, an AI engagement second.
Healthcare cybersecurity practice →If you are a solo inventor, founder, or IP attorney
This pillar is built for organizations with the engineering capacity to absorb a 3-stage methodology and a production hardware blueprint. If you are a solo inventor, founder, IP attorney, R&D scientist, indie SaaS builder, or trade-secret holder who simply does not want ChatGPT, Gemini, or Claude touching your proprietary code, design, recipe, customer list, or unpublished work-product, you have a different fit: a smaller-scoped private AI deployment, not a full Assess-Prototype-Blueprint engagement.
See our private AI options for IP-protective founders, inventors, and patent holders →
Common Questions About AI POC and Prototyping
A consolidated set of the questions we hear most often. The longer answers live inside each stage section above.
What is an AI proof of concept?
An AI proof of concept is a structured, time-bounded engagement to verify that an AI capability can solve a specific business problem in your environment, against your data, under realistic load. It is more rigorous than a demo and less expensive than a full production rollout. The outcome is a clear go or no-go decision plus the telemetry needed to size production hardware.
How is AI prototyping different from a POC?
A proof of concept asks "can this work at all." A prototype asks "what does production cost and how does it break." A prototype runs against your real data, at realistic concurrency, with full telemetry, integrated to your real systems. We use the term prototype because the deliverable is engineered to inform a production hardware blueprint, not just to demo a happy path. See what AI prototyping is and how the methodology works for the full informational walk-through.
How long does an enterprise AI POC take?
Stage 1 Assess takes 5 to 10 business days from kickoff. Stage 2 Prototype runs 4 to 10 weeks depending on data complexity, integration count, and regulatory posture. Stage 3 Blueprint typically takes 2 to 3 weeks of dedicated work after Stage 2 completes. Most full 3-stage engagements complete in 2 to 4 months elapsed time.
How much does an AI POC cost?
Cost depends on scope - cluster compute hours, data-residency requirements, integration depth, and regulated-framework alignment all factor in. We scope every engagement on a 30-minute discovery call so the proposal matches your specific bottleneck. Call (919) 348-4912 to start.
Do you sign BAAs for HIPAA workloads?
Yes. For any engagement that touches electronic protected health information, we sign a Business Associate Agreement before any data sample changes hands. Our cluster is configured to the HIPAA Security Rule and our team has been working under HIPAA postures for two decades.
Do you support CMMC L2 and L3 environments?
Yes, including all three CMMC levels (L1, L2, and L3). Petronella Technology Group is a CMMC-AB Registered Provider Organization (RPO #1449) and our entire team holds CMMC-RP credentials. Prototypes for CUI workloads run inside an enclave aligned with NIST 800-171 for L2 and NIST 800-172 for L3.
Do you keep our data on your cluster, or move it?
Your data stays on our private datacenter cluster in Raleigh for the duration of the prototype. Nothing is sent to a third-party AI API. Nothing is used to train any external model. Access is restricted to the named engineers on your engagement under a signed NDA.
What deliverables do we leave with?
Stage 1: a written readiness report and a one-page go or no-go. Stage 2: a working prototype against your data, a telemetry report, reproducible benchmarks, and a recommendation. Stage 3: a written hardware blueprint, a structured bill of materials, a deployment-topology diagram, an operations runbook, and a 1-year and 3-year TCO model.
What if the prototype shows the project should not proceed?
That is a legitimate outcome and we say so plainly. If the bottlenecks we find are unfixable within reasonable scope, or the business case does not survive the telemetry, the Stage 2 deliverable will recommend stopping. You leave with the data and the report. We would rather lose a Stage 3 engagement than deliver a hardware blueprint that ships you into a production failure.
How do you decide hardware sizing for production?
We extrapolate from the Stage 2 prototype telemetry. We measure concurrency, p50 and p99 latency, GPU memory pressure, KV-cache behavior, retrieval round-trip times, and request shape distribution. We then model production load with your projected user count, peak versus steady-state ratio, and data growth. The blueprint specifies hardware at the lowest tier that meets your latency and concurrency targets with a documented headroom buffer.
Schedule a 30-minute scoping call
Tell us about the bottleneck you are trying to clear. We will tell you whether the methodology fits your scope, your data class, and your timeline. If it does not, we will say so. If you would rather start with a structured diagnostic, the AI Readiness Diagnostic is the on-ramp.
Comparing scope, deliverables, and engagement options? See our AI prototyping services overview.
MSP-Specific Proof of Concept Path
MSPs delivering regulated-SMB AI prototypes to end-clients can use the dedicated Petronella Fleet services-only prototyping ladder - four tiers built around the same methodology. Working prototype, architecture doc, BOM, and compliance memo delivered per engagement. See the MSP Partner Program pricing for the full 4-tier comparison.