Transcript 2

Enter your question below and a representative will get right back to you.

We’re evaluating vendors for a production-grade AI analytics platform and I want to see real, shipped ML systems, not concepts—can you walk me through one public AI case that’s live in enterprise use? Also, what parts of the ML pipeline did your team own end to end (data, training, deployment, monitoring)?

Hello.

For a live enterprise AI case, see our AI-based banking software solution:
innowise.com/case/ai-based-banking-software-solution

We owned data ingestion, model training, deployment, and monitoring end to end.

Before we continue, can you confirm your industry?

We’re an enterprise SaaS provider in the financial services space, focused on predictive risk and churn analytics rather than core banking—how transferable was that banking model to adjacent domains with different data schemas? Also, can you share another public AI case that went beyond a single-use model into a sustained production pipeline?

The banking model’s architecture and lifecycle management are transferable to adjacent domains with different schemas.

For another sustained production ML pipeline, see:
innowise.com/case/machine-learning-in-agriculture

To tailor my response, can you confirm your project type (e.g., new platform, enhancement, integration)?

This is a new platform build, greenfield but expected to hit production within 9–12 months; the agriculture case is helpful, but how did you handle model drift and retraining cadence once it was live with noisy, evolving data? Also, at what scale (data volume / users) was that pipeline operating in steady state?

We implemented automated drift detection and scheduled retraining once drift thresholds were exceeded. The pipeline processed high-frequency data streams and supported enterprise-scale operations.

What’s your primary Phase 1 objective?

Phase 1 is proving predictive accuracy and pipeline reliability with real customer data—offline validation plus one live model in production with monitoring; what MLOps stack did you use there (feature store, orchestration, monitoring), and how opinionated are you versus adapting to an existing stack?

We adapt to client-preferred MLOps stacks or provide our own, including feature stores, orchestration, and monitoring. These systems have been delivered in live production environments.

What’s your current team size?

Core team is ~12 today (data, backend, platform), but we expect to double selectively—how do you typically blend with an in-house team versus running a parallel pod, and who owns production incident response in that model?

We can embed directly with your in-house team or operate as a parallel delivery pod. Production incident response is jointly defined; we typically own first-line response for components we deliver.

What’s your approved budget range?

We’ve approved £900k–£1.3m for Phase 1 inclusive of platform, MLOps, and initial models—how would you structure team composition and milestones against that range? Also, which roles would you staff in the first 90 days versus later?

Within that budget, we’d staff solution and ML architects, backend engineers, and DevOps in the first 90 days, then scale data science, QA, and support roles as milestones are achieved.

What key technical or delivery constraints should I note?

Key constraints are UK/EU data residency, SOC 2–ready controls from day one, and zero tolerance for opaque models in risk scoring—how do you document model decisions and auditability for regulated clients? Also, can you align delivery to a hard pilot date at month 6 without cutting validation corners?

We document model decisions using architecture decision records, sprint evidence, validation reports, and risk logs. We can align delivery to a hard month-6 pilot without cutting validation by front-loading governance and controls.

What’s your target timeline for Phase 1?

Phase 1 target is 24 weeks to pilot (month 6) with checkpoints every 4–6 weeks; does that work for a Discovery Call next week, and can you share two time options so I can pick one?

Here are two options for a Discovery Call next week (America/New_York time zone):

Monday, January 26th, 2026 at 10:00 AM
Tuesday, January 27th, 2026 at 1:00 PM

Please pick the slot that works best. Before confirming, I’ll need your full name.

Tuesday, January 27th at 1:00 PM works.

My name is Oliver Hawthorne — will this call cover delivery governance and commercial structure, or should we plan a follow-up for that?

This call will cover delivery governance and commercial structure in detail—no follow-up needed unless you want to dive deeper.

What’s your best email?

You can use [email protected] .

Will the agenda include MLOps ownership boundaries and post-pilot support expectations?

Yes, the agenda will include MLOps ownership boundaries and post-pilot support expectations.

What’s your best phone number?

You can reach me at +44 20 7946 3187.

Will the invite include a pre-read on your proposed Phase 1 architecture so we can make the call more decision-oriented?

The invite will include a pre-read outlining Phase 1 architecture, governance, delivery approach, success criteria, and exit conditions.

Before I confirm, can you verify the following details are correct?

Name: Oliver Hawthorne
Email: [email protected]
Phone: +44 20 7946 3187

Yes, those details are correct.

Once confirmed, will you also include the proposed success criteria and exit conditions for Phase 1 in the pre-read?

Your Discovery Call is confirmed for Tuesday, January 27th, 2026 at 1:00 PM America/New_York.

The pre-read will include proposed success criteria and exit conditions for Phase 1.