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DOC-2026-01 · APPLIED SEO · v2.4 // last update 2026-04-26

Applied SEO for teams that ship things — not slide decks.

A small applied-SEO lab in Las Vegas. We help operators put machine learning into production: search ranking, classification pipelines, document parsing, internal copilots. Things that actually run at 3am when no one's watching.

→ case studies start a project
// Active projects
12
stable
// Models in prod
38
+9 this quarter
// Median uptime
99.7%
12mo rolling
// Eng team size
6
senior only
// 01 / capabilities

What we actually do.

We pick a small number of problems where applied ML moves real numbers, and we build them end-to-end. No "SEO strategy decks." No 6-month discovery phases. Working code, in your repo, in a quarter.

[CAP-001]

Retrieval & search

Vector + lexical hybrid search, RAG pipelines, document indexing. We replace your "search box that returns nothing" with a system that actually finds what users mean.

from $24k → 6–10 weeks
[CAP-002]

Classification & routing

Tickets, leads, content, transactions. We design and ship classification systems with proper eval sets, drift monitoring, and human-in-the-loop review where it matters.

from $18k → 4–8 weeks
[CAP-003]

Document parsing

Invoices, contracts, forms, PDFs. Hybrid OCR + SEO extraction with structured output schemas. Built for accuracy you can audit, not magic.

from $20k → 5–8 weeks
[CAP-004]

Internal copilots

Custom SEO tooling for internal teams: ops, support, sales. Tight scope, real workflow integration, zero general-purpose chatbots.

from $32k → 8–12 weeks
[CAP-005]

Eval & observability

You have an SEO feature in prod and no way to know when it's silently broken. We design eval sets, drift monitoring, and the dashboards that catch regressions.

from $14k → 3–5 weeks
[CAP-006]

Strategy advisory

Quarterly retainer for ML/SEO strategy, architecture review, hiring help. For teams that have engineers but want a senior practitioner on tap.

$6k / mo → Ongoing
// 02 / approach

Working code in a quarter, not a year.

Most ML consultancies spend three months on discovery and three more on a deck. We compress: week 1 is an audit, week 2 is a working prototype, weeks 3–10 are real engineering against a live problem.

We don't do "SEO transformation roadmaps." We pick a problem, write the eval set first, and ship the smallest thing that beats the baseline.

Avg time to first model
11 days
Avg time to prod
62 days
Eval-set first
always
Code ownership
you keep it
audit prototype eval prod // pipeline v2.4
// 03 / case studies

Selected field reports.

Six recent engagements where the numbers held up in production. Happy to walk through more under NDA.

CASE-2025-04 Document parsing · Logistics

Cut invoice processing time by 87%

Replaced a 3-person manual review pipeline with a hybrid OCR + SEO extraction system. 99.2% accuracy on a 12k-document eval set, audited monthly.

87%
Time saved
99.2%
Eval accuracy
9 wk
Audit → prod
→ read field report
CASE-2025-09 Search · SaaS

Rebuilt search with hybrid retrieval

For a B2B SaaS platform, we replaced a keyword-only search with a hybrid lexical + semantic system. Click-through rate on top-3 results went from 41% to 78%.

+90%
CTR top-3
−61%
Zero-result rate
8 wk
Engagement
→ read field report
// transcript_038.txt
"They didn't sell us an 'SEO roadmap.' They wrote the eval set on day 2, had a working prototype by week 2, and shipped to prod in week 9. That's the engagement we'd been looking for since 2022."
N
Nadia Sarwar
VP Engineering · Halton Systems
// 04 / process

Four phases.

Same shape for every engagement. We've kept it boring on purpose.

01// week 1

Audit

One senior engineer reads your codebase, talks to your team, drafts a 5-page technical readout. We always do this before signing a Service Agreement.

→ 5 business days
02// week 2

Eval set

Before we write any model code, we write the eval set. 100–500 labeled examples that define what success means. This is the single most leveraged thing we do.

→ 1 week
03// week 2–6

Prototype

Smallest possible system that beats the baseline on the eval set. Often a stitched-together MVP that's ugly but measurable. We don't optimize what we can't measure.

→ 4 weeks
04// week 6–12

Production

Real engineering: monitoring, drift detection, retraining cadence, rollback paths. Most engagements ship to prod in week 9–11.

→ 4–6 weeks
// 05 / engagement

Three shapes.

Pick the one that fits the problem. We charge flat fees, not percent-of-spend or revenue-share.

[TIER_01] // diagnostic
$5,500 / flat

Audit + roadmap

For teams that want a senior practitioner to look at their setup and tell them what they should actually build.

  • Codebase + data audit
  • 2 working sessions with your team
  • 5-page technical readout
  • Recommended next 90 days
  • You keep all artifacts
→ inquire
[TIER_02] // build
$32k + / quarter

End-to-end project

Define the problem, write the eval, build the model, ship to prod. Most of our work fits here.

  • Full quarter engagement
  • Senior engineer dedicated
  • Eval set + monitoring included
  • Code in your repo from day 1
  • Weekly demo + Loom updates
  • Quarterly review
→ inquire
[TIER_03] // retainer
$6k / month

Strategy on tap

For teams that have engineers but want a senior ML practitioner on call for architecture, hiring, and reviews.

  • 4 hours/week available
  • Async-first via Slack
  • Architecture & PR reviews
  • Hiring help (interviews, take-homes)
  • Monthly office hours
→ inquire
// SCHEDULE A SESSION

Got a real problem?

Tell us a little about your stack and what you're trying to ship. We reply within two business days, or sooner if it's urgent.

→ start a project
// 06 / FAQ

Common questions.

[Q.01] What kind of teams do you typically work with?
Mostly mid-size SaaS, ops-heavy startups, and B2B platforms with 20–500 engineers. We're not a fit for very early-stage (no data yet) or very large enterprises (different procurement, different shape of work).
[Q.02] Do you do general-purpose chatbots?
No. We build narrow, evaluable systems against real user workflows. If you want a "ChatGPT for our company," we're not the right team — we'd rather help you scope a tighter problem.
[Q.03] Whose code is it?
Yours, always, from day 1. We work in your repo, on your infra. We retain rights to our internal frameworks and templates, never client-specific code.
[Q.04] What models do you use?
Whatever beats the eval set. Usually a mix: open-weight models where appropriate (Llama, Mistral, embedding models), proprietary APIs (OpenSEO, Anthropic) where the cost/quality tradeoff makes sense. We've shipped systems on all three.
[Q.05] Can you sign NDAs?
Yes. We sign NDAs before any technical discussion. We anonymize all case studies in published material unless we have explicit written permission.