AI implementation for operators

Deploy AI inside your business. Stop renting subscriptions.

Most service businesses are paying for six different AI tools that nobody fully uses. We deploy Claude Code, open-source agents, and a custom orchestration layer that automate the work you would otherwise hire software or staff to do, plus our proprietary lead-generation software in your pipeline. Run as a managed service where we handle the agents for you, or transferred to your operator on day 90. Either way, the systems live in your accounts.

Apply for an engagement See the four engagements ↓
The problem

Most businesses don't have an AI strategy. They have an AI subscription pile.

ChatGPT for marketing copy. Claude for legal review. Copilot for engineering. Three custom GPTs nobody opens. A Notion AI feature your team forgot was paid for. None of it talks to your CRM, your billing system, your support inbox. The output stays in browser tabs and never makes it into the actual business process.

That's not an AI problem. The model itself was solved two years ago. What's missing is the orchestration layer: the prompts that match your operations, the data plumbing that lets agents actually act, and an operator on staff who actually runs the system. Building that is what we do.

Developer terminal with code
Inside Formacha / agent stack Vertical 04
What it costs to wait

The compounding tax of staying disorganized.

Companies that have actually deployed AI inside their workflows are reporting 30% delivery velocity improvements, 60% reductions in document-processing costs, and over a month per year reclaimed in administrative time. Companies that are still buying AI subscriptions and hoping their teams use them are paying the same vendor bills with none of the throughput.

The gap is widening monthly. The orchestration layer that takes 90 days to build today will take six months in 2027 because the teams who built it first are already on the next thing. There's no version of the next 24 months where waiting is the right call.

What we run for you

Four engagements. Pick one or stack them.

Each is scoped to a specific outcome. You can start with the audit and decide which engagements to commission. None of these are theoretical, we run versions of all four inside Formacha's own operations.

01

Claude Code deployment

Anthropic's agentic CLI installed across your team. We set up the workspace, write the system prompts that match your operations, train your senior operators on autonomous workflows (planning agents, sub-agents, hooks, MCP servers), and ship the first three internal tools your team will use daily. Typical first deployments: invoice processing, internal dashboards, customer follow-up automation, code review for non-engineering teams, document Q&A.

For teams ready to invest in cutting-edge agentic capability
02

Open-source AI integration

OpenCode, Aider, Cline, n8n with self-hosted open-weight models (Llama, Qwen, Mistral), or any combination. For operators who want lower per-user cost, on-prem deployment, or specific tooling for regulated workflows. We architect the agent stack, deploy the models on your infrastructure, wire up the integrations, and document the runbook so your team owns it after we leave.

For cost-conscious or data-sovereignty-driven teams
03

Custom AI orchestration

The glue layer. We connect your existing tools (CRM, billing, support, marketing) to whichever agent stack you've chosen. We build the internal copilots, document processing pipelines, market intelligence systems, and the agent loops that compound. An agent that watches your inbox, triages, and drafts replies. An agent that monitors competitor pricing and surfaces moves weekly. An agent that reviews your sales calls and grades your reps. The orchestration layer is the part that produces the actual ROI.

For mid-market service businesses where ops is the moat
04

Proprietary lead generation

Our in-house software, deployed for you. Scrapes, scores, enriches, and routes high-intent leads at scale across the channels that actually convert for your vertical (Google Maps, LinkedIn, Apollo, Smartlead, custom signal sources). The same engine we use to build pipeline for our own marketing clients, with attribution tied back to closed contracts. Stand-alone or stacked with the other three engagements.

For service businesses where new client acquisition is the bottleneck
Case studies

Three deployments. Three operating businesses.

Each one replaced a piece of the client's previous workflow that did not scale, and each one runs without the client manually managing the agents day-to-day. Real systems, real businesses, real numbers from the last twelve months.

Ledger AI 01 / 2025-Present Sector · Tactical training

An autonomous ad creative and deployment agent for a tactical training school.

$350K+
Revenue tied to program
course enrollment pipeline
$12
Cost per qualified lead
held steady through scale
3 to 1
Marketing headcount
creative + media buyer + analyst, now one operator
20x
Organic traffic lift
from concurrent SEO work
ClientTacSkills · Tactical training school
EngagementClaude Code deployment + custom AI orchestration
What was brokenSolo marketer running their entire ad operation. Eight to twelve hours a week burned on creative production. Returns plateauing because creative refresh was too slow to keep Meta's algorithm fed. Hiring a creative pod to fix it would have cost more than the program was making.
What we builtAn autonomous ad agent built on Claude Code. Ingests the course catalog and brand DNA, generates platform-specific copy in TacSkills' voice, produces ad images via image-generation models (Nano Banana, FAL), assembles campaigns directly through the Meta Ads API, runs experiments, and pauses underperformers. The marketer reviews and approves at the campaign level, not the asset level. New creative goes live within an hour of approval.
OutcomeOne operator now runs what was previously a three-person creative plus media-buyer pod. Over a hundred enrollments per quarter at a $12 cost-per-lead. Creative volume is no longer the bottleneck, the pipeline is.
Ledger AI 02 / 2025 Sector · B2B lead generation agency

A custom AI-enriched CRM platform for a thirty-person lead generation agency.

4x
Lead volume per client
across 40+ active accounts
60%
Cut in enrichment spend
Clay credits replaced by custom signals
$4,200
Monthly software replaced
consolidated four SaaS tools
+22%
Downstream close rate
only qualified leads hit campaigns
ClientA thirty-person B2B lead generation agency · Pacific Northwest (anonymized)
EngagementOpen-source AI integration + custom orchestration + proprietary lead-gen software
What was brokenManual scraping and enrichment across forty client books. Clay credits ballooning to five-figure monthly bills. CRM hygiene a mess across Salesforce, HubSpot, and three other platforms. No automated quality scoring, so junk leads were burning client trust and ad spend.
What we builtA unified ingest pipeline pulling from Google Maps, Apollo, and LinkedIn into a single hub. An AI enrichment agent adds custom signals per lead (vertical-specific intent, timing, fit score). A quality gate filters before any client campaign sees the lead. Auto-routing to the right Smartlead campaign per client vertical. An analytics layer tracks downstream conversion per source, per agent, per client. The agency runs the whole thing from a single dashboard.
OutcomeReplaced four separate SaaS tools and a chunk of Clay's bill. Lead volume per client account 4x. Downstream close rate up because only qualified leads hit campaigns. The agency operates the same headcount serving 40% more clients than before deployment.
Ledger AI 03 / 2025-Present Sector · Private security operations

An AI dispatch agent for a 200-officer private security firm.

3hr to 20min
Ops manager nightly load
manual report compilation eliminated
0
Invoice disputes
first 90 days post-deployment
220
Officers monitored
scaled from 150 without ops headcount add
100%
Daily reports auto-generated
per-client templated formats
ClientA 200+ officer regional private security firm · Pacific Northwest (anonymized)
EngagementCustom AI orchestration · Twilio + scheduling-system integration
What was brokenOperations manager spending three hours a night reading SMS and voice updates from field officers, manually compiling daily client reports, reconciling time-clock data from the scheduling platform. Errors leaking into client invoices. The firm could not scale headcount without scaling operations cost linearly.
What we builtAn AI dispatch agent integrated with Twilio for SMS and voice transcription. The agent parses officer field updates in real time, extracts structured data (location, time, incident type, action taken), cross-references with the scheduling system's time-clock data, and auto-generates daily reports formatted to each client's preferred template. Anomalies (missed check-ins, unusual incident frequency) get flagged to the ops manager before they become client-facing problems.
OutcomeOps manager nightly workload dropped from three hours to twenty minutes of review. Zero invoice disputes from missing time logs in the first ninety days. The firm scaled from 150 to 220 officers with no operations headcount add. Reporting quality went up, not down.
How we run it

Managed for you, or handed off to your team.

The same deployment, two operating models. Most clients pick managed and never look back.

Managed (most clients)

We deploy and run the systems. You see outputs, leads coming into your CRM, daily reports landing in your inbox, invoices processed, ads running. You don't see (or have to think about) the underlying agents, prompts, or infrastructure. We're effectively a fractional AI team for your business. This replaces software you would otherwise rent, or staff you would otherwise hire.

Owner-operator (when your team wants the keys)

Same deployment, but at the end of the engagement we transfer everything to your in-house operator with full runbooks. You own the agents, the prompts, the infrastructure. We can stay on for high-leverage additions or step back entirely. Your call.

Both are portable

The systems we build live in your accounts, your repos, your infrastructure. You can switch from managed to in-house at any time without rebuild. We don't gatekeep access, hold deployment keys, or charge for the privilege of leaving.

We use what we sell

Examples from inside Formacha's own operations.

We're not an AI consultancy that pivoted from web design last quarter. We deploy this stack daily for our own marketing agency. The engagements we run with clients are the same systems, contextualized.

A

Lead-generation pipeline

Scrapes Google Maps across configured cities weekly, enriches via Clay, routes to Smartlead with personalized cold outbound. Fully autonomous. Built on Claude Code with a custom Python agent layer. Powers Formacha's own client acquisition.

Live, in production
B

Email-campaigns engine

AI-driven newsletter and drip composition with a stop-slop quality filter. Resend integration, segmented audiences, scheduled sends. Composes, edits, and dispatches without manual copywriting. Used for nurture sequences and product drops.

Live, in production
C

Static-site publishing pipeline

AI-driven blog topic ideation via SERP gap analysis, SEO and AEO optimized drafting, automated rendering into static HTML, sitemap regeneration, and one-command deploy. Three weekly publishing cadence with editorial gate.

Live, in production
D

Ad creative generation

Brand DNA briefs, copy generation across platforms, image generation through Nano Banana and FAL endpoints, format adaptation across platforms. End-to-end through Claude Code subagents. Output goes directly to Meta Ads Manager.

Live, in production
How an engagement runs

Three phases. Eight to twelve weeks.

Phase 01 / Weeks 1-2

Audit and roadmap

We map your current tooling, the workflows where AI would actually move the needle, and the integration points that matter. Output is a written 90-day deployment plan with specific tools, agents, integrations, and outcomes. You can take this and execute internally if you want. (Most don't.) The audit is a paid engagement on its own. If you commission the implementation, the audit fee is credited.

Phase 02 / Weeks 3-10

Implementation

We build. Senior operators only, no juniors learning on your spend. Weekly syncs with your team. Live deployment as we go, no big-reveal at the end. You're using working systems by week 4 and watching the rest of the stack come online piece by piece. Source code, prompts, runbooks, and access keys go into your accounts as we ship.

Phase 03 / Week 12 onward

Run it or hand it off

Two paths, your choice. Most clients keep us on as a managed service, where we own the agents, monitor performance, ship new ones as they're needed, and the in-house team only sees outputs (reports, leads, invoices processed, daily summaries). The other path is operator handoff: we train your in-house person on the runbooks, transfer every account, and step back. The systems are portable either way, you can switch from managed to in-house at any time without rebuild.

Fit check

Honest qualification, before either of us spends an hour on a call.

We turn down more engagements than we take. AI deployment done badly is worse than no AI at all. If your business doesn't have the operational complexity, the team capacity, or the budget tolerance to make this worth doing, we'll say so on the audit call.

This is for you if:

  • You run a service business doing roughly $500K to $10M ARR
  • You're tired of "AI strategy decks" with no implementation
  • You have at least one operations-minded person on your team who can absorb a runbook
  • You're willing to invest in a 90-day deployment, not a 30-day pilot that goes nowhere
  • You want to own the systems we build, not rent them through an agency dashboard

This is NOT for you if:

  • You're a pure tech company with in-house ML engineers (you don't need us)
  • You're a solopreneur without operational complexity
  • You're shopping for a $99/month AI subscription bundle, not a real deployment
  • You're hoping AI will fix a broken product or offset bad management (it won't)
About

Why us.

Formacha runs an internal AI stack we built ourselves: lead generation, ad creative, content publishing, email orchestration, ad account management. We deploy Claude Code daily in our own work, including the agent that wrote part of the page you are reading right now.

Two practitioners who have shipped AI inside operating businesses, not slide decks. No dedicated "AI sales engineer" buffer. When you book a call, you talk to the senior operator who would actually deploy your engagement. We have a marketing agency practice (security firms, residential solar installers) and a product practice. The AI consulting work is built on top of the stack we use to run those.

We will never be the biggest AI consultancy. We will always be the one whose senior operator answers the phone.

Investment

Quoted on the audit call after we have seen your unit economics.

Our engagements range from a focused audit-only engagement to full multi-pillar implementation across the four engagements above. We don't list pricing because the right number depends on which engagements you commission, the integration surface area, and the throughput we need to hit. Every engagement is priced so a single durable internal system pays for the entire investment within twelve months.

If we do not see a path to that payback during the audit, we tell you and refund the audit fee. We have done this before.

FAQ

Common questions, answered.

How long does an engagement take?

Eight to twelve weeks for a standard implementation. The audit phase alone is two weeks. We can compress the timeline for more urgent deployments, but we don't recommend it for first engagements.

What does this cost?

Audit-only engagements start in the low five figures. Full implementations across the four engagements scale with scope. Quoted on the audit call after we have seen your numbers. No surprise pricing.

What if my team can't maintain the systems after handoff?

We can stay on retainer for ongoing optimization and new agent additions. The runbooks are also designed to be operator-friendly, written by people who run them daily, not by people who handed them off and moved on.

Do you work with regulated industries?

Yes. We have shipped HIPAA-adjacent and finance-adjacent workflows. The open-source AI integration engagement is specifically designed for data-sovereignty needs.

What happens if Claude Code becomes obsolete?

The orchestration layer is what matters, not the specific agent runtime. We design for tool-portability where it makes sense. The integrations and prompts we build for you are not locked to any single vendor.

Will you sign an NDA?

Yes. Standard mutual NDA before the audit kickoff, before any data or systems get shared.

Can you just build me a custom GPT?

We can, but you probably don't need us for that. Our engagements are for businesses where the agent stack actually changes how the business operates. If you just want a smarter chatbot, you can do that in an afternoon yourself.

Apply

Tell us what you're trying to deploy.

Three minutes. After you submit, you'll be routed to our calendar to book a 45-minute scoping call with the senior operator who would actually deploy your engagement.

Apply / AI Engagement 3 min

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