Frequently Asked Questions

We read every inquiry and reply within a few business days to arrange an initial conversation about your goals, constraints and data. If there is a good fit, we propose a short discovery or data-readiness review with a clear scope, timeline and cost, so you know what you are committing to before any larger engagement.

Timelines vary based on project scope, data readiness, and integration complexity. A proof of concept typically takes 6–12 weeks, longer when the data needs significant reconstruction or the problem is complex. Full production deployments range from 3–9 months, depending on complexity. After our initial discovery session, we'll provide a detailed, phased timeline with milestones and deliverables.

We believe in budget predictability. Rather than billing by the hour — where AI project uncertainties and evolving requirements can lead to unpredictable costs — we agree a fixed monthly fee up front, based on the agreed scope and roadmap. The fee is set in advance and does not depend on hours logged, so there are no surprises, you have financial clarity, and our incentives stay aligned with delivering results. For clearly scoped projects, a fixed-price engagement covering the whole project is also possible.

Yes! We work with organizations of all sizes. For smaller companies, we often start with focused, high-impact initiatives designed to deliver quick wins and measurable ROI. This allows validation of value before expanding scope. Our fixed-fee model helps smaller teams manage budgets effectively.

We have deep experience in oil & gas, healthcare, food & beverage, finance, industrial manufacturing, transportation, logistics and warehousing, business services, biopharma and technology. However, our methodologies are adaptable and our skillsets are transferrable to most industries with data-driven operations. Our methodology is domain-informed but industry-agnostic — centered on extracting value from complex data and operational systems.

Both. Most day-to-day work happens remotely, and we come on-site when it helps the project: discovery workshops, time with the people who run the equipment or processes, and rollout. Our offices are in Dallas and New York.

As partners. Your people know the operation, the data and its quirks; we bring the data science, engineering and AI experience. We work alongside your team throughout, explain our choices as we go, and hand over documentation and knowledge transfer so your team can run and extend what we build.

It depends on the question, but usually historical data that reflects the process or equipment involved, plus a record of the outcomes you care about, such as failures, sales or quality results. You don't need everything in one place. Part of our first step is a data-readiness review: what you have, how well it lines up, and whether it can support the approach.

It rarely is, and that is normal. Logs, work orders and sensor data are often scattered and out of step with each other. Reconstructing a reliable record of past events, such as failures, from those sources is often a substantial part of the work. We tell you early what your data can and can't support.

We are glad to sign a non-disclosure agreement before any data is shared. Wherever possible we work inside your own environment and under your access controls, and we use only the data the project needs. We never publish client names or identifying details without permission; our case studies are anonymized.

We build on your existing stack where we can rather than introducing new platforms for their own sake. Most of our data science and machine learning work is in Python and its standard open-source libraries, and we work across the major clouds: Google Cloud, AWS and Azure.

Yes, when they fit the problem. We build retrieval-augmented (RAG) knowledge assistants and copilots, extract information from unstructured documents, and design domain-specific LLM workflows, with guardrails, evaluation and private deployment options. See our LLM & Generative AI services.

That's exactly where we start. Our discovery process includes an honest assessment of whether AI is the right solution for your challenge. Sometimes the answer is a simpler analytical approach; other times it's a full ML pipeline. We'll recommend what actually makes sense—not what sounds impressive.

A focused, time-boxed test of whether the approach works on your data. We define the problem and the success criteria up front, build a working model, and validate it in a way that reflects real use (for example, on held-out historical data). A proof of concept is how we earn your confidence: it shows on your own data what the approach can and can't do, so that together we can decide whether a larger-scale implementation is worthwhile, before you commit to one.

Success metrics are defined at the outset. These may include cost reduction, revenue lift, time savings, improved forecasting accuracy, reduced downtime, operational efficiency gains, or enhanced data clarity. We align technical milestones with measurable business KPIs.

Yes. Explainability is central to our work. A model's output is only useful if the people who receive it trust it enough to act on it, and that trust comes from understanding why the model said what it did. So our systems show what changed, which factors contributed most, and how confident or severe the result is. The model doesn't make the call. It gives the engineers, planners and operators who know the equipment and the process the evidence they need, and they decide what to do: inspect, schedule maintenance, keep running, or dismiss a false alarm.

Yes. You retain full ownership of all custom models, code, and intellectual property we develop for your project. We believe in building your capabilities, not creating dependencies.

Yes, we offer flexible support arrangements including model monitoring, retraining, system refinement, and performance optimization. Clients retain us for ongoing advisory, continuous improvement programs, governance, and maintenance services to ensure their AI investments deliver sustained value.