Operating context
Biochar
Kiln batches, buyer evidence, carbon receipts.
MRV, feedstock intake, batch evidence, buyer workflows, and carbon-credit reporting.
Eternal TransienceEternal Transience of Intelligence•Transient Labs
We ship AI Agentsthat boost margins in weeks, not months.We shipAI Agentsthat boost marginsin weeks, not months.
A senior product team for founders who need the full build: UX, full-stack, AI, evals, guardrails, deployment, and SOC2 readiness.
Sprint $4,999 · Security Assessment $2,499 · Fractional CTO $9,999/mo
Free quote in 24h
Dedicated senior team
15+ projects delivered
100% IP ownership
$4,999
Fixed sprint
3 weeks
Launch window
SOC2-ready
Delivery posture
Engineers from
Built for
Built for
Industries we work with
Built close to the work.
Biochar kilns, pump dealerships, retail aisles, hardware programs, plant floors, and AI SaaS runtimes all have different truths.
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
Case studies
Agentic Agency
Plugins + skills that deploy swarms across departments.
In the last 2 months, we turned workflows into reusable skills and packaged them as plugins. That's how we run delivery, content, QA, and outbound as coordinated teams of agents.
$20k
last 2 months
67+
skills shipped
4+
departments
Product gallery
Five recent builds are presented below as an interactive board rather than a long scroll rail. Each case opens into a larger mounted view with real screenshot detail.
Active case study
Sustainability SaaS
01 / 05
Emissions Dashboard
Scope3 Global Dashboard
Emissions Dashboard
Scope3 Global Dashboard
Full-stack carbon accounting platform with real-time emissions tracking, supplier management, and ESRS-compliant reporting.
Highlight
Real-time emissions rollups
Highlight
Supplier mix explorer
Highlight
ESRS-ready reporting exports
Browse the five systems
Click or use the arrow keys to move through the active case study.
AI MVP Reality
Where AI products crack under pressure
Demos are easy. Shipping AI users can trust takes guardrails, evals, and latency budgets that hold up outside the lab.
What breaks first
Design for the edge, not the demo.
Budget latency before features.
Treat evals as a live operating system.
Deployment
Edge deployment constraints
Deployment
Edge deployment constraints
Local TTS and VLM quality lag behind cloud, and multilingual coverage stays uneven for privacy-first teams.
The first compromise shows up in quality, coverage, and trust.
Runtime
Latency budget misses
Runtime
Latency budget misses
Cold starts and multi-hop pipelines blow latency budgets, making real-time experiences feel unstable.
Fast demos become slow products the moment load rises.
Workflow
Orchestration reliability
Workflow
Orchestration reliability
Agentic flows loop or break because outputs are not deterministic. Debugging turns into archaeology.
The system looks clever until one branch starts to drift.
Measurement
Static evaluation metrics
Measurement
Static evaluation metrics
Teams want evals that reflect user feedback and business KPIs, not only offline accuracy scores.
Without live signals, teams optimize the wrong thing.
Vendor risk
Provider API instability
Vendor risk
Provider API instability
Provider APIs ignore parameters and rate-limit unpredictably. Fallbacks stop being optional.
Reliability depends on contracts you do not control.
Brand risk
Content drift
Brand risk
Content drift
Generative UI and content pipelines drift toward hallucinations without tight constraints.
Loose generation erodes consistency faster than teams expect.
How we workFixed scopeProduction ready in 3 weeks
Speed with guardrails.
Fixed price certainty.
We ship MVPs fast without brittle AI. Retrieval, evals, observability, and cost controls are part of the build.
Scope lock
Plan and commercial frame get nailed before delivery expands.
Core ship
Real product logic, integrations, and data model land in week two.
Live handoff
Launch, regression pass, and production-ready transfer happen in week three.
Delivery proofBefore the first sprint slips
03
Week sprint
Brief, build, and launch.
The commercial frame, delivery pace, and operating guardrails are set before the build starts stretching.
01
Timeline
3 weeksfrom discovery to live MVP
02
Commercials
Fixed pricescope locked before build expands
03
Guardrails
Built inretrieval, evals, tracing, budgets
Week 1
Frame the product
Week 2
Ship the core loop
Week 3
Harden and launch
Proof-first delivery
Week 1: scope, UX, architecture
Week 2: core build and integrations
Week 3: launch, hardening, handoff
Sprint rhythm
The build reads left to right.
Fewer meetings, fewer mystery phases. Each week has a visible job and a visible outcome.
1
Week 101
Frame the product
We lock scope, user flow, and system shape before code starts branching.
RoadmapUX flowTechnical plan
2
Week 202
Ship the core loop
The MVP turns real: product logic, schema, integrations, and the main user path.
Core buildData modelIntegrations
3
Week 303
Harden and launch
We run the checks, deploy production, and hand over something usable on day one.
Regression passProduction deployLive handoff
1
Week 1
Frame the product
We lock scope, user flow, and system shape before code starts branching.
RoadmapUX flowTechnical plan
2
Week 2
Ship the core loop
The MVP turns real: product logic, schema, integrations, and the main user path.
Core buildData modelIntegrations
3
Week 3
Harden and launch
We run the checks, deploy production, and hand over something usable on day one.
Regression passProduction deployLive handoff
Built into the sprint
The AI work stays operable.
These are not add-ons after launch. They are part of the delivery baseline.
knowledge01
Grounded retrieval
01Chunking, re-ranking, and citations keep answers tied to source material.
Citations first · Less drift
quality02
Evals before launch
02Golden sets, regression checks, and red-team prompts keep quality measurable.
Regression suite · Safer iteration
product03
Structured outputs
03Schemas, tool contracts, and validation loops keep agent output predictable.
Schemas · Validation · Stable UI
budget04
Latency and cost budgets
04Routing, streaming, caching, and fallbacks keep spend and latency in check.
Routing · Caching · Budget caps
observability05
Tracing built in
05Prompt and tool traces make debugging and iteration much faster.
Tracing · Feedback loops
reliability06
Deterministic workflows
06State, retries, and idempotent tools keep multi-step runs reliable.
Retries · Idempotency · Fewer loops
Operations dashboard
The operating view, not a slide.
Shared memory, live traces, and runtime signals make the system legible while it is running.
01
Trace every handoff
Prompts, tools, and memory changes stay visible when runs hop between agents.
02
See the system in motion
Queues, latency, and active jobs are readable while the product is running.
03
Recover without guesswork
Retries, fallbacks, and budgets are designed into the runtime instead of added later.
Shared memoryRuntime traceControl layer
COMMAND_CENTER // V4.2
SYSTEM OPTIMAL
Core Telemetry
CPU Usage
42%
+2.4%
Memory
8.4GB
0.1G
Active Nodes
112
+4
Network I/O
840MB/s
-12%
Request Volume
Agent Workload Matrix
AI-ENGtk_99x
runningGenerate Prisma schema from AST
QUALITYtk_42a
pendingRun regression suite (50 prompts)
GROWTHtk_88z
completedExtract SEO keywords from transcript
Topology Hub
ENG
QA
MKT
SLS
Network Trace
14:22:01.04310.4.12.99→api.anthropic.com
200 OK114ms
14:22:01.89210.4.12.42→db-main-read-replica
200 OK4ms
14:22:02.115worker-pool-a→sqs.us-east-1
202 ACCEPTED22ms
14:22:03.44110.4.12.99→api.stripe.com
200 OK189ms
14:22:04.005cron-runner→api.resend.com
200 OK88ms
root@sys-core:~#
Cross-agent context stays attached
Prompts, tools, and jobs in one view
Latency and cost boundaries stay explicit
Architecture diagramGoverned multi-agent runtime
Structure & Governance
Enterprise Foundation.
Unified Orchestration.
We architect your AI infrastructure using a multi-layered approach. A central orchestrator directs specialized teams operating on top of a unified, compliant data plane.
Layer 01
Specialized Teams
AI Engineering · Growth & Content · Quality Monitoring · Systems Architecture
Layer 02
Orchestrator Factory
Task routing · Shared context · Policy enforcement
Layer 03
Universal Data Plane
Access control · Audit trail · Unified memory
Equilibrium Design
We design the game.
Not just the agents.
Every production multi-agent system is a game — agents with objectives, constraints, and incentives. We architect the equilibrium so the system converges to the right outcome by construction.
Nash equilibria, Stackelberg hierarchies, and observable payoffs are not academic abstractions. They are the engineering primitives behind agent systems that hold up under pressure.
L0Payoff Matrix
The Rational AgentChange the payoffs. Watch the equilibrium shift.
B: Cooperate
B: Defect
A: Cooperate
A
3
B
3
A
0
B
5
A: Defect
A
5
B
0
Nash
A
1
B
1
Both defect — the system converges to mutual loss. Redesign the payoffs.
L3Budget Wars
Intra-Firm DynamicsSwitch allocation policies. See who games the system.
Requested
Allocated
Engineering38% / 92%
Marketing35% / 85%
Sales32% / 78%
Ops36% / 88%
Proportional allocation creates inflation incentives. Agents learn to overstate need — the system diverges from truth.
L5Trust Game
Human x AI EquilibriaToggle observability. Watch trust compound or collapse.
Observability
H
Human50%
AI
AgentCollapseStable
Observable payoffs compound trust. Both sides converge to cooperation.
L6Stackelberg Hierarchy
The Autonomous EnterpriseExpand each layer. See who governs whom.
Stackelberg hierarchyClick to expand
Each layer sets policy before the layers below optimize. The architecture is incentive-compatible by construction.
Services
Simple pricing, framed for real decisions
Fixed scope when the work is bounded. Retainer when leadership matters. Custom scope when the brief needs room.
At a glance
Retainer
Monthly
$9,999/mo
Funded teams scaling AI products
Sprint
3 weeks
$4,999
Solo founders and early teams shipping v1
Assessment
1 week
$2,499
Pre-Series A teams entering enterprise
Custom
Scoped
Let’s talk
Enterprises and complex builds
Retainer
Fractional CTO
Investment
$9,999/mo
Tempo
Monthly
Senior technical leadership for AI products: architecture, roadmap, hiring, and reliability.
Proof artifacts
Architecture and roadmap
Evals, guardrails, cost control
Infra, security, reviews
Best for
Funded teams scaling AI products
For teams that need a driver.
Free architecture review on the first call
Sprint
Production-Grade MVP
Investment
$4,999
Tempo
3 weeks
Ship a product-ready MVP with full-stack architecture, AI guardrails, and automated testing.
Proof artifacts
Product spec and UX flow
Build, evals, and guardrails
Launch, handoff, bug-fix window
Best for
Solo founders and early teams shipping v1
Limited slots. Fixed scope, fixed price.
We scope your MVP on a 20-min call
Assessment
Security Assessment
Investment
$2,499
Tempo
1 week
VAPT and security architecture review with a prioritized remediation roadmap.
Proof artifacts
VAPT and architecture review
Prioritized remediation roadmap
Investor-ready summary
Best for
Pre-Series A teams entering enterprise
Upgrade to full SOC2 certification from $49,999.
Free 15-min security review on the first call
Custom
Custom Scope
Investment
Let’s talk
Tempo
Scoped
Multi-role AI workflows, complex integrations, mobile, or enterprise requirements.
Proof artifacts
Scoped roadmap and milestones
APIs, tools, integrations
Deployment options and support
Best for
Enterprises and complex builds
We’ll quote after a short call.
Free discovery call — no commitment