Boutique AI consulting for SaaS. Document intelligence, computer vision, ML analytics — fixed-scope, fixed-price, fully self-hosted after handoff.
Turn PDFs, forms, and invoices into structured data — so your team stops doing it manually.
Visual QA at scale, anomaly detection, fine-tuned models — for marketplaces, e-commerce, and medtech.
Stop guessing. Start knowing what your data actually says — insight reports, churn models, full stacks.
Most AI consulting is theater. A four-month discovery phase, a slide deck no one reads, and a Jupyter notebook that breaks the moment your engineer touches it. We don't do that.
30 min, free. We map your problem to the right vertical — or tell you we're not the fit.
Fixed scope, fixed price, written timeline. You see what we'll deliver before any contract.
Weekly updates, working previews, no black box. Most engagements ship in 2 to 6 weeks.
Source code transferred. Documentation complete. Your team owns it. We close the engagement.
Client was manually processing 12,000 invoices/month. We built an OCR pipeline with field-level confidence scoring and human review queue. Now 2 engineers manage what took 8.
Marketplace platform had 2 moderators reviewing 5,000 daily image uploads. Custom CV model now auto-flags 89% of policy violations. Queue is manageable. Team focuses on edge cases.
Client had 6,000 users but no visibility into who was leaving. We built a churn model with SHAP explanations. CS team now gets weekly "at-risk" list with reasons. Retention campaigns are targeted, not broadcast.
Most clients break even in 2–4 months. Here's the math.
• Assumes Tier 2 build for selected vertical (OCR: $6,200, CV: $7,500, Churn: $6,200)
• 90% automation rate (10% edge cases still require human review)
• Infrastructure costs excluded (typically $50–200/mo for these volumes)
• Actual results vary by data quality and use case complexity
Not a quote — just a ballpark based on similar projects.
Most consultancies survive on retainers — recurring revenue creates perverse incentives to deliver systems that need the consultancy to keep running.
We don't operate that way. Every package is designed so your team is fully self-sufficient after handoff. If you need help later, we're available on-demand: $250/hr engineering support, $1,200 for model retraining, or custom quotes for larger work. Pay once. Get help. Done.
Yes. 100%.
You receive the full source code (MIT-licensed, transferred to your GitHub org), all trained models, training scripts, and complete documentation. The system runs on your infrastructure. We don't host anything. You're not locked into us.
Depends on your data, but we set minimum accuracy targets:
If we don't hit the target, we don't call it done. No bait-and-switch.
Hosting costs. You pay your own AWS/GCP bill. That's it.
We're upfront about scope: single document type vs multi-type, 5 QA rules vs custom model training, etc. The proposal states exactly what's included and what's not. No surprise invoices.
Express packages: 2–5 days. Tier 2 builds: 3–4 weeks. Tier 3 systems: 6 weeks.
These are fixed timelines, not "it depends." We scope to fit. If your project is larger, we'll break it into phases or give you a custom quote with a clear timeline.
Both. Our sweet spot is mid-market SaaS — Series B to D, roughly.
Early-stage? Start with an Express package ($650–900) to validate the problem. Larger enterprise? We've built systems for teams processing 100k+ documents/month. Scope is scope.
We deliver retraining scripts that run on your infrastructure (cron, Airflow, GitHub Actions — whatever you use).
Monitoring dashboards alert you if accuracy drops. One-command retraining. You own the pipeline. If you want us to handle a retraining cycle, it's $1,200 fixed price — not a monthly retainer.
Standard integrations: yes. Webhooks, REST APIs, S3/GCS storage, CRM exports — all included.
Deep integrations with proprietary internal systems? We'll scope it as custom work. Most clients find the standard webhooks + REST API sufficient.
Technical posts about OCR, computer vision, ML analytics — and how we build systems that don't break after handoff.
Commercial OCR APIs get you 70% of the way. The last 30% is where your data actually lives — line items, tax calculations, vendor-specific layouts. Here's how we bridge the gap.
read_post() →Generic vision APIs are slow and expensive. We built a custom CV pipeline that processes 1M images/day on a single GPU instance. Architecture breakdown and code samples.
read_post() →A churn score is useless if your CS team doesn't know what to do with it. We use SHAP values to give per-user explanations. Here's the pattern we apply to every prediction system.
read_post() →Recurring revenue creates the wrong incentives. When you're paid every month, you're incentivized to build systems that need you. We built Ailayzer specifically to not do that.
read_post() →What your client actually needs when you transfer an ML system. Source code is table stakes. The real value is in reproducibility scripts, monitoring, and documentation that assumes the reader isn't you.
read_post() →Deep dive into a recent OCR pipeline build. Dataset challenges, edge cases we encountered, and how we structured the confidence scoring system for human review fallback.
read_post() →No pitch deck. No discovery phase that never ends. Tell us what you're trying to solve and we'll tell you if we can help, how long it will take, and what it will cost.
Free. No sales pitch. We'll look at your problem and tell you if we can help, how long it will take, and what it will cost. You decide.
30-minute audit call. No pitch, no pressure — just an honest look at your problem and whether we're the right team for it.