Investor and board rooms
Turn the product narrative into a live system: traceable metrics, clean screenshots, and confident operating logic.
Enterprise Agentic Systems from Bits to Atoms
3 weeks
showcase-ready sprint
17 images
real proof assets
100%
client-owned IP
We build product-grade AI control planes for teams whose software touches real operations: fields, factories, stores, hardware programs, audits, and enterprise rooms where the work has to be legible, credible, and live.
3 weeks
focused sprint
$4,999
starter build
100%
IP ownership
Live dashboards with evidence trails, not static pitchware.
Workflow automation mapped to operators, machines, data, approvals, and launch posture.
100x operating layer
LiveRoom signal
A live operating layer, not a slide.
3 weeks
showcase-ready sprint
17 images
real proof assets
100%
client-owned IP
MRV batch 418
Evidence ledger reconciled
Biochar
Pump warranty queue
Three dealer escalations routed
Industrial
Retail demand signal
Inventory action drafted
Retail
Forge routing release
Signed action receipt staged
Hardware
Selected operating marks
Marks from math-heavy teams, deeptech builds, and operating systems shipped fast enough to matter.
Engineers from
Education and operating pedigree without washing out the marks.
High-stakes showcase posture
Built for rooms where hand-wavy AI dies quickly.
Enterprise buyer demos
Show workflows, approvals, evidence, fallbacks, and ownership instead of a brittle prompt demo.
Industrial operator reviews
Map messy field work into queues, dashboards, evidence trails, and exception handling operators can trust.
Industries we work with
Built close to the work.
Field work, test stands, dealer calls, production floors, launch reviews, and SaaS runtimes all have different truths.
Operating context
Biochar
Kiln batches, buyer evidence, carbon receipts.
MRV, feedstock intake, batch evidence, buyer workflows, and carbon-credit reporting.
Operating context
Submersible pumps
Dealer queues and field service made visible.
Dealer operations, service telemetry, warranty intelligence, and field-team dashboards.
Operating context
Retail
Demand signals instead of stale reports.
Inventory copilots, CRM automation, demand signals, and store-level operating views.
Operating context
Hardware programs
BOM changes, routings, and station work made executable.
Forge-style systems for EBOM-to-MBOM handoff, ECO review, supplier impact, routing release, line readiness, traveler signoff, and signed audit trails.
Operating context
Industrial teams
Plant-floor exceptions with owners attached.
Ops dashboards, audit trails, maintenance workflows, and manager-facing control planes.
Operating context
AI-native SaaS
Agentic products with runtime truth built in.
Agents, RAG, evals, observability, billing, roles, and production app architecture.
Selected work
Less theater. More shipped systems.
Product substance, not a maze of generic agency claims.
Biochar + carbon
Climate MRV control tower
Field capture, buyer workflows, audit evidence, project scoping, and emissions reporting in one product surface.
MRVAudit ledgerBuyer ops
Internal AI systems
Agent operating layer
Persistent agents with memory, approvals, tool traces, and recoverable runs for delivery and operations teams.
MemoryApprovalsTraces
Ops intelligence
Industrial dashboard
Thin, useful dashboards that make live status, exceptions, ownership, and next actions visible.
TelemetryExceptionsHandoff
Revenue ops
Retail workflow automation
Lead capture, CRM hygiene, demand planning, and repeatable daily operating workflows.
CRMPlanningDaily ops
Workflow automation
Systems with receipts.
Every workflow needs an operating loop, integrations, and measurable impact. The screen should make the work feel real before anyone reads the details.
$210K
pipeline influenced
45%
faster response
99.7%
match rate
Command Layer
An always-on chief of staff with memory
A persistent agent on your infrastructure that remembers every task, conversation, and automated run. It coordinates content, sales, support, finance, incidents, and onboarding — not as six separate tools, but as one chief of staff that acts on context from yesterday while you sleep.
Business impact
$150K+ in avoidable incident prevention
Memory persists across runs. Context carries across domains.
Deploy a persistent agent on your VPS as your always-on chief of staff
Define role hierarchy: director sets strategy, operators execute, approvers gate, auditors trace
Bind domain playbooks — content pipelines, lead scoring, triage routing, recon checks, incident runbooks, onboarding flows
Agent GatewayVPSSlackNotion
Content & Growth
Publish → Promote → Convert
A centralized control plane tracks content, generates channel variants, and routes high-intent traffic into CRM qualification with consistent brand tone.
Financial impact
$210K annualized pipeline
22% jump in sales-qualified leads, 11 hours reclaimed weekly
Collect article metadata from Notion
Run playbook through the command plane
Generate channel variants and queue web/social/email distribution
NotionAgent GatewayHubSpotGmail
Customer Support
Inbox and Chat → Resolution in context
Incoming support requests are captured, classified, and routed with urgency-aware escalation while preserving every action in auditable logs.
Financial impact
$125K support-cost efficiency
45% faster first response, 32% less manual queue drift
Capture tickets from Zendesk and Intercom webhooks
Classify intent and priority via the agent gateway
Draft response context and assign specialist queues
ZendeskIntercomSlackOpenAI
Sales
Lead to demo in under 90 seconds
Incoming leads are normalized, enriched, scored, and routed into a single, synchronized sales stack so sales teams can call before momentum drops.
Financial impact
$270K+ influenced pipeline
17% increase in booked demos, 28% higher lead quality score
Ingest forms, social, and ad leads
Enrich firmographic and behavior signals
Score using configurable business rules
TypeformHubSpotSalesforceCal.com
Where builds crack
Design for the failure modes first.
These are the failure modes we design around before the product hits real operators, real files, and real customers.
01
Product
Demo logic breaks in production
A clever prototype collapses when real users add messy files, edge cases, and competing workflows.
The first compromise shows up in exception handling, not the hero demo.
02
Reliability
Agent output is not controllable
The system drifts because schemas, retries, approvals, and state are bolted on after launch.
Loose orchestration looks intelligent until one branch starts to drift.
03
Operations
The dashboard does not show truth
Teams get pretty charts but cannot see ownership, blocked work, source evidence, or what changed.
If operators cannot trace a number, they stop trusting the screen.
04
Runtime
Costs and latency are invisible
Model calls, provider failures, and slow multi-step flows become surprises instead of budgets.
Provider risk becomes operational risk unless the fallback path is designed.
Technical patterns
Reliability is a design material.
The confidence comes from retrieval, evals, structure, budgets, traces, and recoverable workflows.
Accurate · Traceable · Reliable
Answers you can trust
AI that cites sources and stays grounded in your data, dramatically reducing errors and hallucinations.
Tested · Measured · Improving
Quality you can measure
Automated testing keeps AI quality visible as the product changes and usage grows.
Structured · Validated · Integrated
Consistent outputs
Structured responses integrate with your systems without broken interfaces or surprise shapes.
Optimized · Monitored · Controlled
Predictable costs
Routing, caching, budgets, and fallbacks keep AI fast without letting spend drift.
Tracked · Analyzed · Improved
Full visibility
Prompt traces, tool calls, and user feedback loops make performance visible after launch.
Reliable · Recoverable · Debuggable
Workflows that work
Multi-step AI processes execute with proper state, retries, recovery, and handoff logic.
Structure and governance
Enterprise foundation. Unified orchestration.
Agents need layers: specialized teams, a central orchestrator, and a governed data plane.
Payoff Matrix
Change the incentives. Watch the outcome shift.
Budget Wars
See who games the system when resources are constrained.
Trust Game
Toggle observability. Watch trust compound or collapse.
Stackelberg Hierarchy
See who governs whom when autonomy increases.
Layer 01
01
Specialized teams
Domain execution stays in dedicated lanes: AI engineering, growth, quality, systems, and operations.
Dedicated lanePermissioned teamsDomain execution
Layer 02
02
Orchestrator factory
A central control plane routes tasks, preserves context, and enforces policy between agents and tools.
Task routingShared contextPolicy enforcement
Layer 03
03
Universal data plane
Access control, audit trails, unified memory, and compliant data boundaries sit underneath the work.
Access controlAudit trailUnified memory
Speed with guardrails
Fixed scope, real launch, visible handoff.
Fewer promises, more proof paths: workflow, build, launch, and ownership are visible from the start.
Sprint rhythm
Week 1
01
Scope the operating loop
We lock the user path, data model, integrations, risk points, and success criteria.
Workflow mapUX pathBuild plan
Week 2
02
Build the core system
The product becomes usable: screens, backend, AI layer, roles, data, and workflow logic.
Core appAI toolsIntegrations
Week 3
03
Harden and hand off
We add evals, traces, fallback paths, deployment, docs, and the next-step backlog.
DeployRegression passHandoff
Capabilities
The AI work stays operable.
Retrieval, evals, traceability, data quality, approvals, and deployment are part of the build baseline.
AI product layer
- RAG and citations
- Tool calling
- Structured outputs
- Human approvals
Operating dashboards
- Status views
- Exception queues
- Audit trails
- Manager workflows
Data systems
- Postgres schemas
- ETL pipelines
- APIs and webhooks
- Data quality checks
Production guardrails
- Evals
- Tracing
- Cost budgets
- Security posture
Services
Simple pricing, framed for real decisions.
Fixed scope when the work is bounded. Retainer when leadership matters. Custom scope when the workflow needs room.
Decision rule
Pick sprint when the path is clear enough to ship.
Pick CTO when decisions need an owner every week.
Pick custom when the system touches data, hardware, teams, or compliance.
3 weeks
Focused sprint
Investment
$4,999
A bounded useful product path with AI, backend, UI, deploy, and handoff.
Proof artifacts
Product spec and UX pathCore app and AI toolsDeploy and handoff
Best for
Founders and operators who need a real v1 without a wandering scope.
Monthly
Fractional CTO
Investment
$9,999/mo
Architecture, reviews, roadmap, delivery leadership, and technical hiring support.
Proof artifacts
Architecture reviewEvals and cost controlDelivery leadership
Best for
Teams scaling AI products where technical decisions now compound.
1 week
Security Assessment
Investment
$2,499
VAPT and security architecture review with a prioritized remediation roadmap.
Proof artifacts
VAPT and architecture reviewPrioritized remediation roadmapInvestor-ready summary
Best for
Pre-Series A teams entering enterprise or procurement conversations.
Project
Custom scope
Investment
Scoped
Larger operating systems, mobile, complex integrations, or industrial data workflows.
Proof artifacts
Milestone roadmapData and API workProduction support
Best for
Industrial, climate, hard-tech, and revenue ops systems with real constraints.
Common questions
Plain answers.
What do you build?
AI products, internal tools, automation systems, data-heavy dashboards, and operating control planes.
How is this different from an agency page?
We scope from the workflow backward: people, data, machines, documents, approvals, and the operating view.
Do you only do software startups?
No. We like software that touches messy real-world operations: biochar, pumps, retail, hard-tech, climate, and industrial teams.
What is included in the sprint?
A fixed-scope product path: UX flow, full-stack build, one core AI workflow, QA, deployment, and handoff.
How do you keep AI reliable?
We use structured outputs, validation, regression evals, tracing, fallbacks, and human approval points where the workflow needs them.
What happens after launch?
You can run with the codebase, or keep us on for roadmap, reliability, hiring, security, and production iteration.
Who owns the IP?
You do. The repo, deploy ownership, keys, and handoff artifacts are yours.