M
Maple54
40hrs Saved/wk · 24/7 Ops · n8n Certified

Your Business on Autopilot.
Powered by AI.

We build custom AI and automation stacks that eliminate 40+ hours of manual work every week. n8n workflows, AI calling, smart chatbots — all connected.

From $2,500 · ROI in 30 days
n8n Certified
MapleVoice AI
24/7 Operations
0hrs
Saved Per Week
0/7
Operations
$0K
Avg Annual Savings
0+
Workflows Built
Automation ROI

See exactly what automation saves you.

Here's a real breakdown from an average client. Manual hours eliminated, replaced by AI agents that work 24/7.

Task
Manual
Automated
Powered By
Lead follow-up calls
15h/wk
0h/wk
MapleVoice
Data entry & CRM sync
8h/wk
0h/wk
n8n Workflows
Email sequences
6h/wk
0.5h/wk
MapleConnect
Lead qualification
5h/wk
0h/wk
AI Chatbot
Report generation
4h/wk
0h/wk
n8n Workflows
Appointment booking
2h/wk
0h/wk
MapleVoice
Weekly Total
40h
0.5h
39.5h saved
39.5
hours
Hours Saved Per Week
$82K
@$40/hr
Annual Cost Savings
<30
days
ROI Payback Period
The AI Stack

Four tools. One connected brain.

Every tool feeds data to the others. Your CRM, calls, emails, and chatbot all work as one intelligent system.

n8n

Workflow Engine — connects everything

MapleVoice

AI calling — books appointments, follows up on leads, leaves voicemails

MapleConnect

Automated email & SMS sequences triggered by behavior

AI Chatbot

24/7 lead qualification, support, and appointment booking on your site

Your CRM

HubSpot, Salesforce, or custom — always in sync

Your Data

Real-time analytics, reports, and dashboards

Case Study

40 hours/week saved. $84K annual savings.
With n8n automation.

Before Automation
Manual data entry15 hrs/week
Follow-up calls12 hrs/week
Report generation8 hrs/week
Lead routing5 hrs/week
Missed leads~30% after hours
Total weekly overhead40 hrs/wk
After Maple54 AI Stack
n8n auto-sync to CRM0 hrs/week
MapleVoice AI calling0 hrs/week
Auto-generated reports0 hrs/week
AI chatbot routing0 hrs/week
After-hours coverage24/7
Annual savings$84,000/yr
Model Selection

Not all AI models
are built the same.

Choosing between Claude, GPT, Gemini, and open-source models is the most consequential architectural decision you'll make. Here's how we pick — by job, not by hype.

ModelProviderStrengthContextWe reach for it when…
Claude 4.6 (Opus/Sonnet)AnthropicReasoning, long-context, code1M tokensAgents, code gen, research
GPT-5 / GPT-4.1OpenAIGeneral reasoning, tool use1M tokensChatbots, orchestration
Gemini 2.5 ProGoogleMultimodal, long documents2M tokensVideo, doc Q&A, analysis
Llama 3.3 / 4Meta (open)Self-hosted, cost-controlled128K tokensOn-prem, compliance-heavy
Mistral Large 2MistralEuropean data residency, fast128K tokensEU-regulated workloads
Embedding / RerankerCohere / VoyageRetrieval, search, RAGN/AVector search, semantic RAG

Updated monthly. Model landscape moves fast — our architecture is provider-agnostic so swapping models is a config change, not a rewrite.

Implementation Path

From idea to shipped AI
in 8 weeks, not 8 months.

The reason most AI projects fail isn't the technology — it's the planning. Teams chase 18-month “transformation roadmaps” instead of shipping one measurable win. We invert that: value in weeks, not quarters.

1
Week 1-2

Prove value

Pick one high-impact workflow with measurable baseline (ticket resolution time, lead qualification rate). Ship a working prototype. Measure. No more 6-month POCs.

2
Week 3-4

Ground in data

RAG over your docs, CRM, or product catalog with Pinecone / Weaviate. Prevent hallucinations by pinning sources. Every response cites the document it came from.

3
Week 5

Add guardrails

Prompt injection filters, output validation (Zod / Pydantic), rate limits, cost caps per session, PII redaction, Claude Shield or equivalent abuse detection.

4
Week 6

Evaluate rigorously

Eval datasets with 100+ real examples, LLM-as-judge scoring, Braintrust or Langfuse dashboards, regression tests on every prompt change.

5
Week 7-8

Ship to production

Gradual rollout behind feature flags, canary analysis, cost monitoring, fallback to deterministic rules if the LLM fails. No big-bang launches.

AI Ethics & Governance

The four commitments
behind every AI system we ship.

AI is moving faster than most governance frameworks can catch up with. These four commitments are our non-negotiables — written into every contract, enforced in every code review.

01

Humans stay in the loop

High-stakes decisions (loan approvals, hiring, medical, legal) always have human review. AI assists — it never autonomously decides outcomes that affect livelihoods.

02

Data stays where you put it

No training on customer data by default. Azure OpenAI, AWS Bedrock, and Anthropic's enterprise API all guarantee zero retention. We write it into the architecture.

03

Transparent capabilities

Users know when they're talking to AI. No fake “virtual employees” with human names and fake photos. Disclosure is built into every system we ship.

04

Measurable accuracy

Every AI feature ships with a published accuracy benchmark, refreshed quarterly. If accuracy drops below threshold in production, the feature disables itself and alerts on-call.

Compliance Posture

Built for regulated industries
from day one.

Healthcare, finance, legal, and enterprise customers can't adopt AI without serious compliance answers. We've done the work so you don't have to relitigate it from scratch.

SOC 2
Type II aligned
Control framework + evidence
GDPR
Article 22 compliant
No automated decisions without consent
HIPAA
BAA available
For healthcare engagements
EU AI Act
Ready for 2026
Risk classification + documentation
PII
Redaction at edge
Presidio + custom classifiers
Audit
Full trace logs
90-day retention default
AI FAQ

What every AI buyer actually wants to know.

Which AI model should my business actually use?+

It depends on the job. Reasoning and agentic work: Claude 4.6 Opus. Tool use at scale: GPT-5. Multimodal + long docs: Gemini 2.5. Self-hosted compliance: Llama 4. We often run multiple models in one workflow — Claude for reasoning, GPT for function calling, a smaller open model for high-volume classification.

How do we stop the AI from hallucinating?+

Three defenses: (1) Retrieval-augmented generation grounds every answer in your source documents with citations. (2) Structured output validation via Zod/Pydantic rejects malformed responses. (3) Evaluation datasets catch regressions before prompts ship. We won't launch a customer-facing LLM feature without all three in place.

Is our data safe if we use AI?+

Yes, when architected correctly. We default to enterprise API tiers (Azure OpenAI, AWS Bedrock, Anthropic Enterprise) that guarantee zero data retention and no training on your data. Sensitive PII is redacted at the edge before ever reaching a model. Your data never trains a public model under our watch.

How much does running AI actually cost?+

For most applications: $0.002-$0.03 per user interaction. A chatbot handling 100K messages/month typically costs $300-$2,000 in API fees. We optimize costs with caching (Anthropic's prompt cache saves 90% on repeated context), smaller models for easy tasks, and batch APIs where latency allows.

Will AI replace our employees?+

Not in any engagement we've run. In every deployment, AI removes the tedious 30-60% of a role (data entry, triage, drafting) and lets humans focus on judgment, relationships, and edge cases. Teams grow faster because each person is 2-3× more productive. That's the pattern across 200+ AI projects we've shipped.

What about the EU AI Act?+

We classify every AI system we ship under the four EU AI Act risk categories (unacceptable, high, limited, minimal) and document mitigations accordingly. High-risk systems get full conformity assessments, model cards, and audit trails. We've been designing for this framework since 2024 — you won't be caught flat-footed when enforcement ramps up in 2026.

Can you fine-tune a model on our data?+

Yes — but 80% of the time we recommend against it. Modern retrieval (RAG) plus good prompts handles most use cases without the cost, ops burden, or model staleness of fine-tuning. When fine-tuning is the right answer (high-volume classification, style adaptation), we ship via OpenAI fine-tuning, Anthropic custom models, or LoRA on open-source.

Do you build voice agents / AI callers?+

Yes. We build on top of MapleVoice, our proprietary voice stack, which handles real-time phone conversations with sub-400ms latency. Use cases: inbound lead qualification, appointment reminders, customer-support tier-1, after-hours coverage. Average handled-call rate: 68% without escalation to a human.

Transparent Pricing

Automation that pays for itself.

Every plan includes setup, training, and 30 days of optimization. Average ROI payback: less than 30 days.

Starter

$2,500

1-2 automations · single workflow

1 n8n workflow (up to 10 nodes)
CRM integration
Email notifications
Basic reporting
Setup + training
30 days support
Start Starter →
Most Popular

Growth

$5,000

Full stack · multiple workflows

Up to 5 n8n workflows
MapleVoice AI calling
AI Chatbot integration
Multi-channel sequences
Custom dashboard
Weekly optimization
90 days support
Start Growth →

Enterprise

Custom

Unlimited · dedicated AI team

Unlimited workflows
Full AI stack deployment
Custom AI model training
Enterprise integrations
Dedicated automation engineer
SLA-backed uptime
12-month support
Get Custom Quote →

All plans include setup + training · ROI guarantee · Month-to-month

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should do for you.

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