Ag Express Electronics — Technical Partnership Proposal

Agentic Intelligence
for Agricultural Electronics

A platform that diagnoses faster, retains technician expertise, and compounds knowledge across every repair — built on your hardware, owned by you.

87 Features 4 Phases 3.88× Delivery Leverage MVP: Q1 2027 On-Prem First
01 — The Opportunity

The gap between institutional knowledge and scalable throughput

The challenge today
Expertise lives in heads, not systems
  • Senior technicians carry diagnostic knowledge that is not captured anywhere
  • Bench diagnosis time is long because each tech starts from scratch
  • Four sites, no shared knowledge layer — the same problem is solved four times
  • Seasonal peaks overload intake and stretch turnaround windows past customer tolerance
  • No closed loop between what was diagnosed and whether the fix actually held
The platform opportunity
An agentic knowledge layer built on your data
  • Capture every technician insight as a structured graph entry — permanently
  • Guided bench diagnosis from a corpus of real cases, traces, and resolved failures
  • One shared intelligence layer across all four sites from day one
  • Voice intake agents that handle call volume during peak season without headcount
  • Outcomes feed back into the model: every repair makes the next diagnosis sharper
“The harness catalog work, the trace library, ten years of repair tickets — that institutional knowledge is already there. The platform turns it into something every technician can use, on every bench, the first time.”
02 — The Platform

Five nodes. One intelligence layer.

Each node is a governed business capability with its own data contract and agent scope. They share the knowledge graph and accumulate value together over time.

⚙
Command Center
On-prem control plane with local model serving, knowledge graph, vector index, and full data sovereignty. The foundation everything else runs on.
Foundation / Phase 1
📜
Corpus
Structured ingestion of repair tickets, harness catalogs, OEM manuals, failure mode taxonomies, and technician expertise into a queryable graph.
Knowledge / Phase 1–2
🔬
Bench
CAN/J1939 hardware interface, trace replay, response differencing, guided diagnostic flow, verdict with confidence, and quote generation.
Diagnostic Core / Phase 1–2
📞
Intake
Voice agents handling inbound repair calls with transcription, intent routing, ticket creation, status answers, and warm human handoff.
Voice / Phase 2
📉
Operations
Repair queue routing across four sites by capacity and capability, turnaround forecasting, parts demand signal, and ISO evidence generation.
Ops / Phase 2–3
03 — Why Swift Innovation

Built for this kind of work

Agentic infrastructure at production scale, with a track record in complex knowledge-intensive domains.

Agentic infrastructure at production scale
We build and operate multi-agent systems for knowledge-intensive domains. The control plane, agent governance, and observability layer are already proven — they're not being designed for this project.
On-prem first, cloud optional
Your data and models stay on your hardware. We have direct experience deploying local inference servers, graph databases, and CAN interface hardware in industrial environments.
Domain knowledge compounds
Every bench session, every captured technician insight, every repaired unit goes back into the corpus. The system gets materially better every month. That is not an AI marketing claim — it is the design.
04 — The Speed Advantage

3.88× delivery leverage. Parallel workstreams. Earlier value.

Agentic build methods compress delivery time without compressing quality. We run multiple workstreams simultaneously instead of sequentially.

Traditional sequential build
One workstream. Every phase waits for the last.
Infrastructure setup
↓ waits
Data ingestion & corpus
↓ waits
Bench application
↓ waits
Voice intake
↓ waits
Operations layer
Agentic parallel build (Program tier)
Three workstreams. Infrastructure, corpus, and bench run together.
Command Center
→
Bench MVP
Corpus ingest
→
Knowledge graph
Intake voice agents
→
Ops queue
✓ MVP live: end of Q1 2027
3.88×
delivery leverage
1,900
agent hours
7,372
human-equivalent hours
87
features, 4 phases
Capability Node Agent type Phase
Knowledge graph populationCorpusIngestion agent1
Guided bench diagnostic flowBenchDiagnostic agent2
Verdict and repair recommendationBenchVerdict agent2
Inbound call intake and routingIntakeVoice agent2
Repair status answeringIntakeVoice agent3
Queue routing across sitesOperationsOps agent2
Corpus quality and retrainingCommand CenterQuality loop agent3
05 — Ownership

Everything built goes to you. No lock-in.

At the end of every phase, Ag Express Electronics owns the full stack outright. The platform is designed for independence, not dependency.

  • ✓
    On-prem control plane and model servingRunning on your hardware, under your control, no cloud dependency for core operations
  • ✓
    Knowledge graph and vector indexAll of your data, your ontology, your structured institutional knowledge — exported, backed up, migratable
  • ✓
    Bench application source codeFull desktop application codebase in your hands at the end of Phase 2
  • ✓
    Corpus and training dataAll ingested documents, traces, and generated training examples belong to Ag Express
  • ✓
    Agent configurations and governance layerAll agent charters, prompts, and approval rules are version-controlled and exportable
  • ✓
    Documented runbooks and transition rightsThe continuity package (CC-18) is delivered in Phase 2: runbooks, escrow, rehearsed handover
  • ✓
    Infrastructure and hosting freedomNo SaaS layer, no per-seat licensing, no metered API calls on your own data
  • ✓
    Fine-tuned domain model (Phase 4)Domain-adapted model trained on your corpus, eval harness included, weights owned by you

Swift Innovation's role is to build, deploy, and transfer — not to operate indefinitely as a dependency. The retainer funds engineering capacity; the intellectual property transfers continuously.

06 — The Plan

Four phases. MVP at end of Q1 2027.

Timelines shown for the recommended Program tier (3 engineers, 3 parallel workstreams). Foundation and Build tiers extend each phase proportionally.

Phase 1
Foundation
Oct 2026 — 1 month
  • On-prem control plane
  • Local model serving + router
  • Knowledge graph + vector index
  • Ag electronics ontology
  • Ingestion pipeline + classification
  • Baseline measurement
13 features · 214 agent hrs
Phase 2 — MVP
Core Systems
Nov 2026 — Feb 2027
  • Bench hardware interface + desktop app
  • Trace replay + response differencing
  • Guided diagnostic + verdict
  • Corpus: technician knowledge + trace lib
  • Voice intake + call routing
  • Repair queue orchestration
33 features · 554 agent hrs
Phase 3
Scale
Q2–Q3 2027 — ~4 months
  • Field tablet application + offline mode
  • Behavioral model runtime
  • Advanced voice capabilities
  • Web + marketing content pipeline
  • Parts demand signal
  • Corpus quality loop
28 features · 528 agent hrs
Phase 4
Platform
Q4 2027 — ~4 months
  • Domain model fine-tune
  • Component-level fault localization
  • Dealer tenancy + licensing
  • Electronics residual risk scoring
  • External data product distribution
  • Internal knowledge assistant
13 features · 604 agent hrs
07 — The Team

Right-sized allocation at every tier

Each retainer tier includes dedicated engineering capacity plus shared leadership. The agent layer is active across all tiers and multiplies throughput without adding headcount.

Role Foundation Build Program
Senior Engineers 1 2 3
Architecture & PM leadership ✓ ✓ ✓
Agent build layer (dedicated hrs/mo) 40 hrs 80 hrs 120 hrs
Parallel workstreams 1 2 3
On-prem hardware support ✓ ✓ ✓
AI dev tooling overlay ✓ ✓ ✓

Leadership time (architecture, PM, and QA) is shared across clients but fully dedicated to Ag Express during active sprint planning, review, and handoff. Engineers are assigned full-time to this engagement per tier.

08 — Investment

Three tiers. One objective: MVP by end of Q1 2027.

All tiers reach the same platform. The tier determines the pace and team size. The recommended tier — Program — targets MVP delivery within the winter trough window when operational disruption is lowest.

Foundation
$10k/mo
1 engineer + leadership · 40 agent hrs/mo · 1 workstream
Phase 1 Foundation (~3 months)$30k
Phase 2 Core (~9 months)$90k
MVP timeline~12 months
Phase 3 Scale (~9 months)$90k
Phase 4 Platform (~9 months)$90k
One-time onboarding fee$10k
Total through MVP $130k
Best for a measured start. MVP delivery by ~Oct 2027.
Build
$20k/mo
2 engineers + leadership · 80 agent hrs/mo · 2 workstreams
Phase 1 Foundation (~2 months)$40k
Phase 2 Core (~5 months)$100k
MVP timeline~7 months
Phase 3 Scale (~5 months)$100k
Phase 4 Platform (~5 months)$100k
One-time onboarding fee$10k
Total through MVP $150k
Two parallel workstreams. Bench and corpus built concurrently.
Program — Recommended
$30k/mo
3 engineers + leadership · 120 agent hrs/mo · 3 workstreams
Phase 1 Foundation (1 month)$30k
Phase 2 Core (4 months)$120k
MVP timeline5 months
Phase 3 Scale (4 months)$120k
Phase 4 Platform (4 months)$120k
One-time onboarding fee$10k
Total through MVP $160k
Three workstreams, winter trough build window. MVP live end of Q1 2027.
09 — Infrastructure

Infrastructure: own vs. rent

The on-prem design eliminates recurring AI compute costs. The choice is a one-time capital outlay versus paying cloud API rates every month, per engineer, indefinitely.

On-prem hardware (one-time capital cost)

Equipment Notes Est. one-time
GPU inference server24–48 GB VRAM; runs all primary models locally$8,000–$12,000
NAS / bulk storageCorpus ingestion, vector snapshots, backups$1,000–$2,000
Managed network switchVLAN isolation for inference traffic$500–$1,000
Cabling and UPSRack power protection and cable management$300–$600
Hardware totalOne-time; no depreciation schedule applied~$10,000–$15,000

Alternative: cloud API tokens (per engineer, per month)

Cloud API route
Pay-as-you-go tokens
Per engineer / month $1,000–$1,500
Foundation (1 eng) $1,000–$1,500/mo
Build (2 engs) $2,000–$3,000/mo
Program (3 engs) $3,000–$4,500/mo
Cost is ongoing and scales directly with team size and usage. No ceiling.
On-prem route (recommended)
Own the compute
Capital cost (all tiers) ~$10k–$15k once
Recurring inference cost $0/mo
Payback vs. Foundation 8–15 months
Payback vs. Program 3–5 months
Hardware pays for itself before MVP launches at the Build or Program tier. Every month after that is pure savings.

Post-launch steady-state operating costs

Line item Notes Est. monthly
On-prem compute (inference)Ag Express hardware — no cloud bill$0
Knowledge graph databaseNeo4j Community or equivalent on-prem$0–$150
Vector storeWeaviate or Chroma self-hosted$0–$100
Voice / telephony (Intake)Per-minute billing; scales with call volume$200–$600
Backup and offsite storageEncrypted snapshots; object storage$50–$150
Frontier model API (overflow)Optional; local models handle primary load$100–$300
Monitoring and alertingSelf-hosted Grafana + Prometheus$0–$50
Post-launch total estimate~$350–$1,350/mo

Voice costs vary with inbound call volume and season. The on-prem model serving eliminates per-token API costs that would represent the largest line item in a cloud-first design. Infrastructure estimates are reviewed at the end of Phase 1 once actual call volumes and workloads are instrumented.

10 — Estimation Methodology

How the numbers work

Features are sized S / M / L / XL at scoping. Each size maps to a fixed agent-hours budget and a human-equivalent hours reference. The compression ratio is the ratio of human time required without the agent layer to agent time with it.

Size Scope Human equiv Agent hrs Compression
S Focused feature, clear spec, no schema changes ~8 hrs 2 hrs 4.0×
M Cross-cutting feature, API + UI + tests ~31 hrs 8 hrs 3.9×
L Multi-system capability, new node or data schema ~116 hrs 30 hrs 3.9×
XL Platform-level capability, novel hardware or model interface ~388 hrs 100 hrs 3.9×
Phase Features Agent hours Human equiv Program timeline
Phase 1 — Foundation 13 214 831 1 month
Phase 2 — Core (MVP) 33 554 2,150 4 months
Phase 3 — Scale 28 528 2,049 ~4 months
Phase 4 — Platform 13 604 2,344 ~4 months
Total 87 1,900 7,374 ~13 months

Agent hours are the actual build hours purchased in the retainer. Human-equivalent is what this scope would require without agentic methods, used to calibrate scope against a conventional engagement. Sizes are assigned at kickoff and locked for the phase; scope changes mid-phase require a re-size and are tracked in the feature matrix.

11 — Next Steps

Four steps to a signed engagement

01
Discovery call
Review the proposal and feature matrix together. Walk through Phase 1 and 2 scope. Confirm hardware inventory and site access.
02
Agreement and tier selection
Select the retainer tier. Sign the services agreement. The agreement includes IP assignment, continuity provisions, and the data classification policy.
03
Infrastructure provisioning
Deploy the on-prem control plane and establish the knowledge graph. Run the baseline measurement (CC-17) to instrument current performance before anything changes.
04
Phase 1 kickoff
Sprint 1 begins. Corpus ingestion starts on the first available repair ticket dataset. Command Center goes live at the end of the first month.
Engagement Lead
Charles Brubaker
[email protected]
Project Support
Swift Innovation Team
[email protected]
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