AI is easy to start.Hard to control in production.

Unmeshed is the execution layer for AI work. Govern which workflows call which models, optimize token spend, and connect every dollar of AI cost to a business outcome.

Start for Free
Unmeshed

Connect Systems Without Glue Code.

A Short Diagnostic

If any of this sounds familiar, we should talk.

Every major tech wave produced a new control problem. Cloud created FinOps. SaaS created software sprawl. AI is producing token spend, agent sprawl and workflow governance all at once.

01

Are you spending too much on AI tokens?

Most enterprise AI bills doubled in the last 4 quarters with no proportional revenue lift.

02

Do you know which workflows are creating the bill?

Provider dashboards show usage. They don't show which business workflow is responsible.

03

Are your teams using models where they actually create value?

A model is a tool, not a default. Many AI calls are routing decisions a function would handle.

04

Are agents and tools running without enough control?

Agentic loops can fan out in ways nobody approved and nobody is alerted until the invoice arrives.

05

Is your board asking how effectively you're spending tokens?

Same way Finance asks Engineering about cloud spend. Same answer should be ready.

06

Do you need a better answer than "usage is growing"?

You need cost per workflow, cost per outcome, and policies that cap both.

Answered yes to any of these?

Talk to usSee how it works

A Single Run, Step-by-Step

Every step through a model is a step you're paying for.

Here's one execution of an order-processing workflow. Toggle between the way most teams build it today and the way Unmeshed lets you build it.

Step 01

parse_order

code $0.000

Step 02

validate_totals

code $0.000

Step 03

lookup_customer

code $0.000

Step 04

classify_risk

llm $0.027

Step 05

route_decision

code $0.000

Step 06

enrich_address

code $0.000

Step 07

check_inventory

code $0.000

Step 08

choose_carrier

code $0.000

Step 09

write_audit

llm $0.008

Step 10

commit_order

code $0.000

Cost · This Run

$0.035

2 llm steps

Steps Billed

2 / 10

Projected · 500k Runs/Mo

$210k

annual

See full pricing breakdown

The Concept

From tokenmaxxing to token demaxxing.

Tokenmaxxing is what AI looks like at the start: more calls, more agents, more chains, more spend. Token demaxxing is what it looks like once it's in production: intentional, governed, value-driven execution.

Tokenmaxxing

Every request goes to a model.

  • AI calls for routing, parsing, validating
  • Agents fan out without budget caps
  • Provider dashboards as the only governance
  • Bill grows faster than business value
  • "Usage is up" is the only KPI
Uncontrolled Execution

Token Demaxxing

Models only where they add value.

  • Deterministic code for routing & parsing
  • Agent budgets, scopes & tool allow-lists
  • Per-workflow cost attribution & caps
  • Spend tied to a business outcome
  • "$ per outcome" is the KPI
Governed Execution

Agent Budgets

Cap tokens at the agent.

Per-agent monthly ceilings with auto-throttle and downgrade.

support_agent68%
research_loop112% ▲
classifier_v322%

Tool Allow-lists

Scope every tool call.

Agents can only reach the tools you bind to them. No shadow calls.

tools bound4 / 12
crm.lookupdb.readvector.searchslack.notifyfs.writeshell.exechttp.post

Cost per Outcome

Tie spend to value.

Attach a business KPI to every workflow. Watch $/outcome trend.

ticket_resolved
$0.043$0.061
invoice_extracted
$0.011$0.038
lead_qualified
$0.190$0.42
vs metered

Put the concept into practice

Explore the platformUnmeshed vs others
Built by creators ofConductor•originally built atNetflixand trusted by
American ExpressJPMorgan ChaseAtlassianUWMCoupangGE HealthCareDTDLT LogoClariAmerican ExpressJPMorgan ChaseAtlassianUWMCoupangGE HealthCareDTDLT LogoClari

The Execution Model

Deterministic where you can. AI where you must.

Every step in an Unmeshed workflow declares what it is code, model, human, decision so the runtime knows what to charge for, what to retry, and what to audit.

parse_input
crm_lookup
classify_intent
route_decision
assign_agent
Unmeshed
4 code steps
$0.000 each
1 llm step
$0.018 billed
cost / workflow
$0.018 vs $0.090
parse_inputcode · $0.000
crm_lookupcode · $0.000
classify_intentllm · $0.018
route_decisioncode · $0.000
assign_agentcode · $0.000
Deterministic step · $0LLM step · only where needed
Start from a workflow template

Your Complete Development And Runtime Platform

05:00
05:01
05:02
05:03
warehouse_nightly_load
05:04
05:05
partner_file_ingest
05:06
05:07
invoice_settlement_post
05:08
05:09
eod_reconciliation_run
05:10
05:11
compliance_report_gen
05:12
05:13
payroll_batch_close
05:14
05:15
warehouse_nightly_load
05:16
05:17
partner_file_ingest
05:18
05:19
invoice_settlement_post
05:20
05:21
eod_reconciliation_run
05:22
05:23
compliance_report_gen
05:24
05:25
payroll_batch_close
05:26
05:27
warehouse_nightly_load
05:28
05:29
partner_file_ingest
05:00
05:01
05:02
05:03
warehouse_nightly_load
05:04
05:05
partner_file_ingest
05:06
05:07
invoice_settlement_post
05:08
05:09
eod_reconciliation_run
05:10
05:11
compliance_report_gen
05:12
05:13
payroll_batch_close
05:14
05:15
warehouse_nightly_load
05:16
05:17
partner_file_ingest
05:18
05:19
invoice_settlement_post
05:20
05:21
eod_reconciliation_run
05:22
05:23
compliance_report_gen
05:24
05:25
payroll_batch_close
05:26
05:27
warehouse_nightly_load
05:28
05:29
partner_file_ingest

Batch Jobs with Orchestration Control

Read More About batch Jobs with Orchestration Control

Where You Are on the Curve

Different AI maturity, same control problem.

Wherever you are on the adoption curve, the question is the same: who is controlling the execution layer that turns AI usage into AI spend? Pick the stage closest to yours.

Stage 01

Early AI adoption

Pilots, copilots, and chat integrations are spreading across teams. Usage is growing fast but governance and spend visibility haven't kept up.

pilots7
ownersunknown
active models3
monthly bill$3.4k

Get control before AI usage spreads across teams and tools.

Stage 02

AI in workflows

Models are embedded inside production paths: support triage, document extraction, and decisioning. Cost compounds quickly as every request triggers an LLM call.

support_triage$38k
doc_extract$29k
intent_route$14k

Control the execution layer that creates repeated AI spend.

Stage 03

AI agents

Tool-using agents executing multi-step tasks. Fan-out, retries and tool-calls drive the bill.

research_loopcap reached
vector.searchdb.readhttp.postllm.call
fan-out depth3 / 5
token budget18k / 32k

Govern agentic execution before token usage becomes unpredictable.

Stage 04

Enterprise AI in production

AI is operational across the org. Boards ask FinOps-style questions about token spend.

$ / outcome · q3$0.043
vs target-18%
workflows under cap142 / 156
active teams12 / 14

Create a controlled operating layer for AI workflows across the organization.

Find the right starting point for your stage

See the agentic platformTalk to the team

Ready To Launch

Save tokens and your budget.Unmeshed helps enterprises control the workflows that create the AI bill.

Our forward-deployed engineers can map your current AI workflows, find where the bill is being created, and stand up Unmeshed in weeks not quarters.

Start for FreeBook a Demo