| Typical timeline | 12–18 months |
| Typical cost | $1–3M per migration |
| Failure rate | ~60% miss original timeline |
| Auditor acceptance | Often rejected — manual transformations are not defensibly traceable |
| Security stance | Non-negotiable: data cannot leave their network, cannot use public AI, cannot ship schemas to a SaaS vendor |
Accenture, Deloitte, IBM. Slow, expensive, every project rebuilt from scratch — knowledge walks out the door.
Informatica, Talend. Don't understand GxP, no audit defensibility, no domain knowledge of quality systems.
Veeva-only, capacity-constrained, expensive. Doesn't help non-Veeva targets.
Fast but unauditable. Regulators reject them. Tribal knowledge that doesn't survive turnover.
| Where does our data go? | Will any record, attachment, or schema leave our network — even temporarily, even to your cloud? |
| Who has access? | Can your engineers see our data? Can your AI vendor see it? Can anyone outside our company see it? |
| What does the AI see? | If you use AI for mapping, does our quality information flow through OpenAI, Anthropic, or any external service? |
| Can we prove what happened? | When a regulator asks why a record was transformed a particular way, can we show them — for every record? |
| What if you go away? | If your company disappears next year, does our migration stop working? Are we locked in? |
| How do we control access? | Can our existing identity system (AD, Okta) control who logs in? Can we revoke access using our normal offboarding process? |
| Where does data go? | Nowhere. MAiGRATE installs as a self-contained appliance inside the customer's data center. No callback. No upload. No telemetry. |
| Who has access? | Only their own staff. Configuration happens on their machines, with their data, by their people — with our SE alongside them, on their network. |
| What does the AI see? | Their own AI environment, nothing else. MAiGRATE plugs into the AI service they have already approved through their own procurement and security review. |
| Can we prove what happened? | Yes — for every record, automatically. Per-record evidence pack: source value, target value, rule applied, AI reasoning, validation result, target response. |
| What if you go away? | Nothing breaks. Migration is a one-time event. Output is theirs forever. Zero ongoing dependency on us, our cloud, or anything we control. |
| How do we control access? | Their identity provider runs the show. Their AD/Okta. Same offboarding processes that protect their other systems also protect MAiGRATE. |
Anything that can be mapped without AI is mapped without AI. We don't use AI where simple rules work — it would be slower, more expensive, and harder to defend to a regulator. Every AI decision is logged in the per-record evidence pack with the exact prompt, response, and reasoning.
No code is written per customer. Ever. The SE captures the workshop output as configuration files and loads them into the customer's installation.
| Source connection | Where their old system lives — URL, credentials, how to read from it |
| Target connection | Where their new system lives — URL, credentials, what to write to |
| Field mapping rules | For each field in the source, which field in the target it goes to (with any transformation) |
| Lookup tables | Translation tables for codes, picklists, categories — provided by their QA team |
| Validation rules | What makes a record valid for the target system; what to do if it isn't |
| AI guidance | Customer-specific style guide for the AI — terminology, conventions, edge cases |
| Workflow choices | Batch size, dry-run mode, parallel-run mode, retry policy, cutover date |
Same install pattern. Same delivery shape. Same SE playbook. The next three slides walk through what this looks like for each of the three illustrative scenarios.
| Week 1 | Install MAiGRATE in their on-premise data center. Wire it into their identity provider. Place credentials in their secrets vault. |
| Week 2 | Field mapping workshop with their QA team. Configure Customer A's specific rules. |
| Week 3 | Dry-run on a 500-record sample. Their subject-matter experts review every AI mapping decision. Rules tightened. |
| Weeks 4–6 | Phased migration in 10,000-record batches. Each batch produces an evidence pack. Auditors sign off batch by batch. |
| Weeks 1–2 | Same install pattern. Configure the connection to their MasterControl system. |
| Week 3 | Load Customer B's configuration: their version-chain mapping rules, signature-record handling, document type lookups, terminology guide. |
| Weeks 4–5 | Dry-run, mapping refinement, full migration, evidence pack delivery, sign-off. |
| Week 1 | Same install. Reuse the TrackWise connection from Customer A — no new work needed there. |
| Week 2 | Configure the ServiceNow connection for Customer C's environment. |
| Week 3 | Load Customer C's configuration: different target object model, validation rules, parallel-run mode enabled. |
| Weeks 4–6 | Parallel run begins. Every change in TrackWise syncs to ServiceNow within 90 seconds. Runs 90 days, then cutover. |
Regardless of source, target, or cutover strategy, every engagement produces the same set of deliverables. This consistency is part of the contract.
The infrastructure is one virtual machine. That's it.
| Their existing on-premise / VMwareThey already have host capacity | ~$0 marginal |
| Their Azure / AWS subscriptionOne mid-size VM plus 500 GB of SSD storage | $500 – $700 / mo |
| Migration window only (6–12 weeks)Scale down or decommission after cutover | $1,000 – $2,000 total |
The customer mental anchor is the $1–3M they would otherwise pay a consultancy. Our pricing lands clearly under that.
| MAiGRATE license | $250K – $400K | One-time. Right to run the appliance for this migration. |
| Implementation services | $150K – $350K | One-time. SE configures, runs dry-runs, supports cutover, hands off. |
| Optional 12-month support | $50K – $80K | Bug fixes, SE hours during stabilization period. |
| Total per customer | $450K – $830K | Roughly half the cost of the consultancy alternative. |