AI Implementation Suite
Eleven productized services for businesses with real operations — from a 2-4 week audit through multi-quarter transformation engagement to per-function operational AI. Engineering shop, not agency.
AI implementation done by an engineering shop. For businesses with real operations — solo trades to mid-market enterprises.
The suite is structured to meet buyers where they are. Some buyers want an audit before committing; the Implementation Audit is fixed-fee, 2-4 weeks, packaged. Some buyers want the strategic partnership shape that competes directly with the AI-transformation consultancies; the Transformation Engagement is multi-quarter, principal-led, integrated. Most buyers eventually want one or more of the operational products — document intelligence, customer support AI, finance ops AI, the rest.
The positioning principle: real operations, not company size. An HVAC company with 30 trucks and real customer data is a serious operating business. A mid-market manufacturer with 200 employees is a serious operating business. A $200M software company is also a serious operating business. The suite scales across that range; the products inside it pick the function and pick the depth.
Works with the powerful models offered by Anthropic, OpenAI, or other providers. The integration discipline matters more than the model choice; models change every quarter and the framework around them shouldn't.
Horizontal overlap is real. Document Intelligence here is the horizontal version of FO's K-1 Extraction and PE's Diligence Red-Flag Engine. Finance Operations AI here is the horizontal version of FO's Invoice & Bill-Pay. Where the buyer has a vertical-specialized version available, we cross-link to it.
Strategy & engagement
How the relationship starts. A fixed-fee audit for businesses that want clarity before committing; a multi-quarter strategic partnership for businesses ready to commit to a transformation.
- I·01Flagship
AI Transformation Engagement
A multi-quarter engagement covering strategy, implementation, integration, and team training. Principal-led (Bogdan in the discovery and architecture phases, ongoing oversight throughout). Outcome: AI deployed in production across the use cases identified during strategy.
6–18 month engagement · principal-led - I·02
AI Implementation Audit
A productized 2-4 week audit — operations mapped, candidate AI use cases identified, each scored on effort and impact, packaged as a roadmap document with prioritized action plan.
2–4 week sprint · packaged roadmap
Document & knowledge intelligence
Horizontal LLM-and-extraction products for the document corpus most established businesses accumulate — ingestion and routing, internal knowledge access, contract and compliance review.
- II·01
Document Intelligence Engine
A document-AI pipeline that ingests the business's document corpus, classifies by type, extracts the relevant fields, routes to downstream systems with confidence-graded review queue.
8–14 week build · ongoing operation - II·02
Internal Knowledge Assistant
An LLM-powered Q&A assistant on the business's internal document corpus — team-facing chat interface, permission-aware, with answer-with-citation so users can verify before acting.
6–10 week build · ongoing model maintenance - II·03
Contract & Compliance Review Engine
An AI review layer for the contract and compliance document corpus — extracts canonical clauses, flags off-standard terms against the business's precedent, checks policy compliance against regulatory and internal standards, surfaces anomalies for legal review.
6–10 week build · ongoing operation
Workflow & operations automation
One product per business function — process orchestration, customer support, sales and marketing ops, finance ops, HR ops, field operations. AI for every function that runs your business.
- III·01
Process Automation Agents
A multi-step agent orchestration layer — workflows expressed as agent-coordinated processes, integrated with the business's existing systems, with human-in-loop checkpoints where the workflow needs them.
8–14 week build · ongoing operation - III·02
Customer Support AI
An AI layer over the existing ticket stack — routes tickets by category and urgency, drafts initial responses for routine queries, flags edge cases for escalation, surfaces context from prior interactions and product documentation.
6–10 week build · ongoing operation - III·03
Sales & Marketing Operations AI
An AI layer across the RevOps stack — predictive lead scoring, CRM data hygiene, outbound drafting and sequence-optimization, attribution analysis. Sits on top of the existing CRM and marketing automation.
8–12 week build · ongoing operation - III·04
Finance Operations AI
An AI layer over the finance-ops workflows — invoice ingestion and posting, expense categorization, AP/AR reconciliation, payment matching, fraud-pattern detection. Integrates with the business's accounting system (NetSuite, QuickBooks, Sage, Xero, custom).
8–12 week build · ongoing operation - III·05
HR Operations AI
An AI layer over the People-Ops workflows — resume screening with bias-audit, interview scheduling, draft generation for employee communications, onboarding workflow automation. Integrates with the existing ATS and HRIS.
6–10 week build · ongoing operation - III·06
Field Operations Automation
An AI layer for the field-service operating stack — scheduling and dispatch optimization, automated customer communications, quote-generation drafting, follow-up automation. Integrates with the standard field-service software (ServiceTitan, Jobber, Housecall Pro) or with the business's existing custom stack.
6–10 week build · ongoing operation
How a ai implementation suite engagement runs.
Each product has its own engagement shape — typical length, what you get, who staffs it. Across the suite, the constants are the same.
- Lead
- Bogdan
- Cadence
- Async-first
- Engagement
- Per product
- Bar
- Production
Principal engineer. Most architecture and most code ships through one keyboard.
Weekly check-ins, written updates between, calls when the decision needs the room.
Shapes listed on each product page — typically multi-week builds with ongoing operation.
Async correctness, capacity under burst, observability at every boundary.
If this fits the operation, the next step is one call.
We'll talk scope and fit. If we're not the right fit, you'll know fast.
Bogdan and team · async-first · OP—2026