Bring the problem. We pick the AI that fits.
Teams that know when AI is appropriate and when it is not. Based on your problem, we will tell you what you need — and from there, you make the decision.
Problem Framing & Feasibility
We start with your business problem, not a technology. We frame it into something measurable, set an honest baseline, and give you a straight feasibility read — including when the answer is that AI is not the right tool yet.
Right-Fit Approach Selection
We weigh the candidate approaches — a predictive model, a generative / LLM system, computer vision, language and document AI, or plain automation — against your problem and data, and recommend the one that fits. Vendor-neutral, problem-led, never approach-by-fashion.
Custom AI Build
We build the chosen solution end to end — training or fine-tuning a model, wiring up a retrieval or agent pipeline, or assembling the right models behind your application — with the surrounding software that turns a model into a usable product.
Data Foundation for AI
AI is only as good as the data behind it. We prepare and structure the data your solution needs with reproducible pipelines, so the system can be retrained and trusted later — not built on a one-off export nobody can recreate.
Production Deployment
The solution is packaged behind a clean, versioned API or embedded in your application, with a staged rollout and a rollback path. Your own systems call it through a simple interface — this is the step where most AI projects stall, and closing it is the core of the engagement.
Monitoring, Governance & Handover
Performance and drift monitoring so you know if the system degrades, plus a model registry and runbooks. PII is identified and handled at the data layer to support your CCPA and data-governance obligations, and everything is documented so your team can operate it.
How We Choose
Give us a problem and receive back a solution.
You do not need to know what AI model you need — just bring us your problem. Weighing all our options, we will tell you what we think, even if that means an AI model may not be the appropriate service.
You bring
Your problem
A business problem and the data you have — not a chosen technology. We frame it and set an honest baseline before recommending anything.
We weigh the candidate approaches
You get
One built solution
The chosen approach — built, deployed to production, monitored, and handed over as a system you own. Not a demo, and not five options to choose between.
You bring
Your problem
A business problem and the data you have — not a chosen technology.
We weigh the approaches
You get
One built solution
The chosen approach — built, deployed, monitored, and handed over as a system you own.
Click any approach to see when it fits and what we build.
AI That Fits the Problem
We choose the approach because it suits your problem and your data — not because it is the approach in the headlines this quarter. Sometimes the best answer is a small model or a simple rule, and we will build that instead of an expensive one you do not need.
Across the Production Gap
AI built to ship — serving infrastructure, versioning, and a rollback path designed for real use, not a prototype that wins a meeting and then lives forever in a notebook. Reaching production is the design target from day one, not an afterthought.
Start Small, Prove It First
You do not have to commit a six-figure budget on faith. A short feasibility sprint tests the riskiest assumption on your real data and gives you an evidence-backed go / no-go — so the full build starts only once we both know it can work.
You Own What We Build
Built on open frameworks with a documented codebase, model registry, and runbooks your team can run. No black box and no dependency on us to keep it alive — you can take it in-house whenever you choose.
Honest About When AI Isn’t the Answer
If the data is not ready, or a non-AI solution wins, we will say so — with the reasoning shown. We would rather tell you that early than sell you a model that quietly underperforms. That honesty is the relationship we are building.
Key Capabilities
- Problem framing and AI feasibility assessment with an honest baseline
- Right-fit approach selection across ML, generative AI, vision, and language
- Custom model training, fine-tuning, and transfer learning
- Retrieval-augmented generation (RAG) and LLM application development
- Computer vision and document / language understanding pipelines
- Reproducible data and feature pipelines for AI
- Leakage-safe evaluation and honest accuracy reporting
- Model serving (real-time API and batch) and application integration
- Monitoring, drift detection, and a retraining path
- PII-aware data handling and AI governance support
Technologies
Engagement Models
AI Feasibility Sprint
- Problem framing and success-metric definition
- Right-fit approach recommendation (or a no-AI verdict)
- Data readiness and feasibility check on your real data
- Go / no-go recommendation with a build estimate
Custom AI Build
- Data foundation and pipelines for the chosen approach
- Model build, fine-tuning, or AI pipeline development
- Honest evaluation against your success metric
- Production deployment behind a versioned API or in your app
- Monitoring, model registry, runbooks, and handover
AI Build + Managed
- Everything in Custom AI Build
- Ongoing monitoring and incident response
- Scheduled retraining and model refresh
- Periodic performance reviews and tuning
- Direct Slack / Teams channel with the AI team
Frequently Asked Questions
We know we want to use AI but not what kind — is this the right place to start?
Yes — that is exactly who this engagement is for. You do not need to arrive knowing whether you need a predictive model, a generative AI system, computer vision, or document AI. You bring the problem and your data; we run a structured assessment and recommend the approach that fits, with the reasoning shown. If you already know the modality, our specialist tracks for ML, generative AI, computer vision, and document AI go straight to the build. If you do not, start here and we figure it out together before any large commitment.
How do you decide which AI approach is right for our problem?
We start from the problem and the data, never from a favorite technology. We look at what you are trying to predict, generate, classify, or automate; what data you have and its quality; your latency, cost, and accuracy needs; and your risk tolerance. That points to one of a few approaches — a custom predictive model, a generative / retrieval system, vision, language and document AI, or plain automation. We weigh them openly and recommend the fit. Crucially, "you do not need AI for this — a simpler approach wins" is a legitimate, common, and honest outcome.
Will you actually ship something to production, or just build a demo?
Production is the design target from the first day, not a phase we hope to reach. We frame the problem against the system that will use the result, build data pipelines that run the same way in production as in development, and package the solution behind a versioned API or inside your application with a staged rollout and a rollback path. The gap between an impressive prototype and a system real users depend on is where most AI efforts die — the engagement is structured around closing it, not around the demo.
How do we start without betting the whole budget on something unproven?
Start with the AI Feasibility Sprint. In about two weeks we frame your problem, recommend the right approach, check whether your data can support it, and build enough on your real data to give you an honest go / no-go with a build estimate. You leave with a clear decision and the evidence behind it — not a six-figure commitment made on faith. The full build begins only once we both know the approach can work, so your larger spend follows proof rather than precedes it.
Do we own what you build, or are we locked into you?
You own it. We build on open frameworks with a documented codebase, and where a trained model is involved we hand over the model registry, pipelines, and a runbook your team can run. Handover includes structured knowledge transfer so your engineers can operate, retrain, and extend the solution without us. If you choose the managed option, that is a convenience, not a dependency — you can take operations in-house at any point.
How do you handle our data, PII, and AI governance?
PII is identified at the data-pipeline layer and handled according to your requirements — masking, exclusion, or aggregation depending on what the solution actually needs — and datasets are versioned so there is a traceable record of what data the system used. This supports your CCPA and internal data-governance obligations. We architect the data layer so privacy decisions are explicit and documented rather than buried. We do not provide legal compliance certification; we build the AI and data layer so it supports your governance program rather than working against it.
Have a problem you think AI can solve?
Book a 30-minute call. Tell us the problem and what data you have — we will talk through whether AI fits, which approach makes sense, and whether a feasibility sprint or a full build is the right place to start.