A
Targeted fix
There is a specific permissions, data, integration, evaluation or deployment issue your team can address directly.
For established SaaS with an AI prototype
Your AI prototype works. Now make it production-ready.
Identify the security, tenancy, data, evaluation, integration and architecture gaps blocking your AI capability from production, then turn them into a sequenced plan your team can ship.
30 minutes · No prep required · No pitch deck
Assessment starts from the working prototype
- Production Blocker Map
- Minimum Unlocks
- Next Production Step
Why prototypes stall
The demo is not the hard part. Production is.
- 01The prototype works in a controlled demo, but breaks when real users, tenants or permissions enter the picture.
- 02The team can show promising output, but cannot yet measure reliability well enough to release it.
- 03The AI is isolated from the workflows, data and systems it would need to be useful in production.
- 04Security, deployment, observability or governance questions keep pushing the release date.
Start here
Start by finding the actual blocker.
The 30-minute AI Production Review starts with the prototype and one important workflow, then narrows the issue before anyone commits to a larger engagement.
- Walk through the prototype and one important workflow.
- Identify likely production blockers.
- Locate the issue across product, technical and operating constraints.
- Recommend a targeted fix, a broader production plan, follow-on engineering, or no Datics engagement.
What happens next
The review tells you what the next step actually is.
B
AI Scorecard
$5,000, two weeks: your AI tested in staging on 50 of your own cases, with ranked fixes, draft security answers and a fixed quote to take it to production. The fee counts toward it.
C
No Datics engagement
If your team already has what it needs, we will tell you.
Need a broader product and engineering plan?
The Planning Sprint ($5,000, two weeks) maps the capabilities and quotes the build.
From working demo to a clear next production step.
- 01
Prototype baseline
- 02
Map blockers
- 03
Identify minimum unlocks
- 04
Define next production step
Proof
Different production constraints. Same brownfield reality.
Three examples: hardening a working AI prototype into a multi-clinic product, adding production AI around an existing regulated core, and bringing AI deeper into live healthcare workflows.
Healthcare · Clinical Intake SaaS
Taking an intelligent intake prototype to a production multi-clinic SaaS
Working AI prototype → production multi-clinic SaaS
- Trajectory: Prototype to production SaaS
- Tenancy: Multi-clinic isolation
Financial Services · Investment Operations Software
Adding production AI around a regulated investment platform without replacing its .NET core
Production AI added around an existing .NET core
- Existing core: .NET application retained
- AI delivery model: Python microservices around the core
Dental & Veterinary · Practice Communications SaaS
AI communication intelligence inside a live practice platform
AI that acts inside a live healthcare workflow
- Appointments recovered: 311 across six locations in a documented example
- Recovered revenue: $100K+ associated with previously missed opportunities in the documented example
Who this is for
A focused engagement for an established product with a real production constraint.
Good fit
- Established vertical B2B SaaS
- Live customers and recurring revenue
- Existing AI prototype, POC or experimental capability
- Internal product and engineering team
- Production blockers around security, tenancy, permissions, data, evaluation, deployment or integrations
Not a fit
- Greenfield AI startup
- Generic chatbot project
- Prototype with no real product or customer workflow behind it
Why Datics
Brownfield AI is our specialty.
- AI and data since 2018
- Production work inside existing SaaS
- Existing cores retained rather than rewritten
- Product, architecture and AI considered together
Questions
Before you book
- What should I bring to the review?
- A working prototype or demo, one important workflow, and enough product or technical context to explain where it is getting stuck.
- Does our prototype need to be working?
- It should be far enough along that you can show the intended workflow or behavior. It does not need to be production-ready.
- What kinds of production blockers can you help identify?
- Typical blockers include permissions, tenancy, data access, integrations, evaluation, reliability, security, observability, deployment and product fit.
- Is this a technical architecture review?
- Partly, but not only. We look at the product workflow, the architecture around it, and the production constraints that determine whether the capability can safely ship.
- What happens after the 30-minute review?
- We will recommend the most sensible next step: a targeted fix, an AI Scorecard, a Planning Sprint, or no Datics engagement.
- What is the AI Scorecard?
- A fixed $5,000, two-week test of one AI feature you have built, live or not, in staging, on 50 of your own cases: answer quality, data separation, misuse, cost per user and response time. You get a scorecard, ranked fixes, draft security answers and a fixed quote. It is not a compliance audit or certification.
- Can Datics help implement the next step?
- Yes, if there is a fit. After an AI Scorecard or Planning Sprint we can take the capability to production at a fixed quote, and the fee counts toward it. The review itself is diagnostic.
Find what is actually blocking production.
Bring the prototype, the workflow and the constraints. In 30 minutes, we will help determine what stands between the demo and something customers can actually use.