Built for every team that runs AI

Build on frontier intelligence. Own the system around it.

Engineering owns a stable runtime while frontier, specialist, and customer-controlled intelligence remain available behind it. Each call clears its quality bar, is inspected in context, verified before the application consumes it, and returns with a signed receipt. The intelligence record learns your workload while your provider keys and contracts stay yours.

Talk to the foundersSee how it deploys
call_9f27e4 · production governed
classifystructured extraction · cell 23 of 32
intelligencemodel-h45 · bar met
policyclean · failover armed
verify0.97 · passed
settle$0.0041 · signed
<1ms
added latency
Signed
evidence per call
1
endpoint
Open
provider relationships stay yours
Learning
record improves with use
87%1
reference workload reduction
BYOK
keys and contracts stay yours

The intelligence landscape changes every week. Your application should not have to.

tiers gives the application one stable, governed contract while preserving access to frontier, specialist, and customer-controlled intelligence. New options are evaluated against your quality bar before production work changes.

01

Adopt without rewrites

Add an intelligence source, replace one, or move a workload without rebuilding the application around another provider SDK.

02

Degrade without failing

Provider brownouts, quota pressure, and access changes trigger cross-vendor failover while the mission is still running.

03

Let repeated work become software

When a pattern can be governed deterministically, routine math, date logic, transforms, and known templates can resolve without model spend.

One call. Four operating decisions.

Production systems need economic control, runtime policy, independent verification, and evidence that survives the request.

Price discovery

Classifies the work and uses the lowest-cost intelligence that clears the required quality bar.

Runtime inspection

Evaluates tool calls, HTTP requests, and subprocesses before execution, across the full session rather than one prompt at a time.

Verification

Checks the output against task intent, schema, and rolling quality baselines before the application consumes it.

Settlement

Records the intelligence, cost, policy, and verification in a tamper-evident receipt for every call.

The engineering objection: another proxy in the critical path.
tiers is designed as reversible infrastructure. One base URL in. One base URL out. It adds less than one millisecond to the reference path, keeps your provider relationships intact, and can fail open or closed by policy.
client.base_url = "https://YOUR_TIERS_ENDPOINT"
# same SDK · same messages · governed response
decision model-h45 · verify passed
receipt 0x9f27e4 · cost $0.0041

Your teams can adopt frontier intelligence when it earns the work while the application keeps one contract with the runtime. Every verified outcome makes the system more specific to you.

Watch a governed call.

The full lifecycle, from the seat where it happens: classified, inspected, verified, settled, receipt signed. Try the failure path. Try turning governance off.

WITHOUT tiers
$0.0000
WITH tiers
$0.0000
SAVED
$0.0000
you@prod ~ tiers session
GOVERNED

Simulated session using verified reference-workload figures.1 When the proxy is live, the same component runs against your tiers endpoint and nothing changes except the transport.

The same call gives the rest of the company what it needs.

Finance gets cost attribution. Security gets policy evidence. AI leadership gets quality telemetry. Nobody asks engineering to instrument the same workload four different ways.

One governed runtime for every intelligence source.

One environment variable. The first call is governed. If anything fails, automatic fallback to your original provider.

Talk to the foundersSee how it deploys

1. Cost reduction measured on the tiers reference workload against a signed, immutable 14-day counterfactual: $6.10 per day governed versus $47.00 per day ungoverned. Platform average across measured cohorts: 83.08%; optimized cohorts: 87.9%. Results vary by workload.

Make every AI dollar explain itself.

Finance owns verified unit economics for AI work. Budget before execution, attribute every call, verify the work delivered, and compare governed spend against a signed parallel baseline.

Talk to the founders
inference · month to date reconciled
ungoverned counterfactual$47.00 / day
governed spend$6.10 / day
verified savings$40.90 / day
tiers fee2% of governed
receipt coverage100%
87%1
reference reduction
~335x
savings to fee
$0
provider markup
Per call
cost and owner attribution
Per loop
budgets and reserves
Signed
governed counterfactual
2%
of governed spend

AI spend becomes a governed operating line, not an invoice archaeology project.

Every receipt connects the call to a team, deployment, task, model, provider, cost, policy result, and quality outcome. Finance can see where spend went and whether the work cleared the bar.

01

Control spend before it happens

Per-session, per-team, and per-deployment budgets trip before a runaway loop becomes an invoice.

02

Defend the savings number

The signed counterfactual runs the same workload governed and ungoverned, then signs both cost streams call by call.

03

Keep the economics neutral

No model markup, no seat fee, and no incentive to choose a more expensive provider.

The counterfactual is the product.

A generic benchmark cannot tell you what your workload would have cost. tiers measures the identical workload in parallel, preserves the evidence, and makes the difference auditable.

baseline_window 14 days
ungoverned $47.00 / day
governed $6.10 / day
quality_bar maintained
signature sha256:30:82:01:0a...
same calls · same period · two signed cost streams
The finance objection: every vendor claims savings.
The 87% is not presented as a universal promise. It is the measured result on the reference workload. The durable claim is the instrument: tiers gives each customer a signed counterfactual for its own traffic.

The goal is not a smaller AI program. It is more intelligence per dollar, with controls that survive scale. Every dollar of waste removed can fund the next deployment.

One receipt, four stakeholders.

Engineering gets decision and latency. Security gets policy evidence. AI leadership gets quality. Finance gets the complete unit economics without another reconciliation step.

Put a signed meter in front of inference spend.

The first call is governed, with automatic fallback to your original provider. The system produces your signed counterfactual on your own traffic.

Talk to the founders

1. Reference workload result from a signed, immutable 14-day counterfactual: $6.10 per day governed versus $47.00 per day ungoverned. Platform average across measured cohorts: 83.08%; optimized cohorts: 87.9%. The savings-to-fee ratio divides measured savings by a fee equal to 2% of governed spend. Results vary by workload.

Control the session, not just the prompt.

Security owns session evidence created before actions execute. tiers evaluates behavior across the session, enforces provider and residency policy per call, verifies the output independently, and signs the evidence.

Talk to the founders
session_7a21 · policy trace inspecting
tool · read_file(config.yaml)allowed
http · api.vendor.com/v2allowed
subprocess · git diffallowed
trajectory · credential stagingblocked
receiptsigned · retained
Pre-act
controls before execution
Session
trajectory-aware
Signed
evidence retained
Pre-exec
policy before action
Session
trajectory-aware defense
Per call
residency enforcement
Signed
evidence by default

Gateways see requests. tiers sees what the agent is trying to do.

A multi-step attack can look harmless one call at a time. tiers keeps state across the mission, evaluates the trajectory, and intercepts the action surfaces where intent becomes execution.

01

Stop actions before execution

Tool calls, outbound requests, and subprocesses are evaluated in-process, not reconstructed from logs after the fact.

02

Verify outside the provider

Quality and task alignment are checked by tiers, independent of the model that produced the output.

03

Produce the evidence automatically

Intelligence, policy, verification, residency, and cost arrive as one tamper-evident receipt.

Policy follows the call.

Control is applied at runtime, where model, provider, geography, data class, tool access, and mission state are all visible together.

Session-level detection

Intent and anomaly signals accumulate across the complete mission rather than resetting on every request.

Sovereign execution

Residency and provider policy are enforced call by call, not assumed from a contract signed months earlier.

Independent drift detection

Rolling quality baselines surface provider-side regressions before they become a customer-support pattern.

Fleet control

Pause a session, a deployment, a team, a provider, or the entire fleet from the same operating plane.

The security objection: we already have a gateway and DLP.
Those controls remain useful. tiers covers the blind spot between them: the runtime sequence of model decisions and agent actions. It does not replace the perimeter. It governs the inference path.

Security stops being the team that blocks agents and becomes the reason the company can deploy them. The control and the evidence are created by the same call path.

The receipt is also the finance record and the quality record.

The company does not need separate instrumentation for security, cost, and model governance. Each function reads the same signed event.

Make runtime policy part of every AI call.

Review the architecture, threat model, deployment modes, and evidence format with the team that built it.

Talk to the founders

Threat-family and inspection-latency figures reflect tiers' internal production architecture and mapping. Regulatory and standards alignment should be evaluated against each deployment's facts, jurisdiction, and control environment.

Build an intelligence system that learns your work.

AI leadership owns the learning curve. Define the quality bar once; tiers verifies production outcomes, turns them into customer-specific learning signal, and keeps frontier intelligence available when the work demands it.

Talk to the founders
customer intelligence · production learning
extraction · verifiedmodel-f25 · 0.97
code review · verifiedmodel-s46 · 0.94
research · frontiermodel-r56 · 0.91
math · learned patterndeterministic · $0.0000
customer specialistevaluating against bar
Observe
every governed outcome
Learn
customer-specific signal
Own
portable intelligence
Task first
quality before preference
Continuous
quality evaluation
Neutral
no model or token sales
87%1
reference workload reduction

Frontier intelligence and independence can coexist.

Use the strongest available systems where they earn the work. At the same time, preserve the quality labels, production outcomes, and learning signal that make your own intelligence estate more capable over time.

01

Make quality an owned capability

Your task taxonomy, quality bars, and production outcomes become portable intelligence rather than provider-specific configuration.

02

Evaluate continuously

Frontier, specialist, and customer-controlled systems can be compared against the same production quality bar. Changes follow measured outcomes, not launch-day benchmarks.

03

Stay neutral by construction

tiers operates no models, sells no tokens, and earns nothing from where a call lands except that it landed correctly.

Four functions of inference governance.

A plural intelligence market needs more than access. It needs price discovery, runtime inspection, verification, and settlement.

Price discovery

Find the lowest cost that can complete this task at the required quality, not the cheapest intelligence in the abstract.

Runtime inspection

Inspect the session for manipulation, unsafe actions, and provider or supply-chain anomalies.

Verification

Verify what was delivered against the requested outcome before the application consumes it.

Settlement

Clear the call with a signed record of intelligence, policy, quality, and cost.

The AI leadership objection: we already chose a frontier provider.
Keep it. tiers does not compete with the intelligence your teams rely on. It independently measures what each workload needs, verifies what was delivered, and preserves the learning so the customer benefits from every call.

Use frontier intelligence where it earns the work. Turn every governed outcome into intelligence that compounds for you.

The same operating plane works for the teams that have to approve it.

Engineering gets a stable interface. Security gets runtime control. Finance gets the counterfactual. The AI strategy no longer depends on one function carrying the whole argument.

Turn production work into an owned capability.

Start with the work already running. Govern it, verify it, and preserve the evidence that makes your intelligence system more specific to your organization.

Talk to the founders

1. Cost reduction measured on the tiers reference workload against a signed, immutable 14-day counterfactual. Platform average across measured cohorts: 83.08%; optimized cohorts: 87.9%. Results vary by workload.

Turn AI spend into an institutional capability.

Executives own the institutional record for cost, quality, security, and evidence across every intelligence source and every team. Every verified outcome makes the program more efficient, more defensible, and more specific to the company.

Talk to the founders
company AI · governed live
calls governed100%
outputs verified99.6%
policy interventions18
reference cost reduction87%
evidence coveragesigned · per call
1
governed record
Learning
with every outcome
All
teams and workloads
Cost
measured and attributable
Quality
verified before use
Risk
controlled at runtime
Evidence
signed on every call

AI adoption is outrunning the institutional record around it.

Teams can add intelligence in an afternoon. The company still needs a coherent answer for spend, quality, security, ownership, and auditability. tiers makes those controls part of the call itself.

01

Scale without budget surprise

Budgets, governed decisions, deterministic execution, and per-call attribution keep usage growth from turning into unexplainable spend.

02

Move faster without lowering the bar

Teams can use more providers and more capable agents because quality and policy are continuously enforced.

03

Give the board one answer

Every AI call is governed, inspected, verified, and settled under one operating model.

Governance is an expansion strategy.

Lower unit cost makes more workflows viable. Runtime trust makes more teams willing to deploy them. Evidence makes more regulated use cases possible. Governed inference expands the surface area of AI the company can responsibly use.

For the board

A measurable answer for AI economics, exposure, and control coverage.

For operators

One runtime rather than a different stack for each team, intelligence source, and vendor.

For customers

Outputs that were verified and policies that were enforced before the system acted.

For regulators and auditors

A durable evidence object produced by normal operations rather than reconstructed later.

The executive objection: is this just cost cutting?
No. Cost is the fastest way to prove the runtime works, but the strategic outcome is larger: every governed outcome becomes evidence the company can use to improve its own intelligence estate.

The answer to “what is our AI strategy?” becomes durable: use the best available intelligence, govern every outcome, and keep the learning.

Bring the staff into the same operating model.

Each function gets the information it needs from the same governed call, without creating five separate AI programs inside the company.

Make AI intensity compound.

Start with the work already consuming the most intelligence. The first call is governed, with automatic fallback to the original provider. Every verified result strengthens the record.

Talk to the founders

Illustrative executive-console metrics are presentation examples except for the reference workload cost result. The 87% reduction was measured against a signed, immutable 14-day counterfactual; platform average across measured cohorts was 83.08%, with optimized cohorts at 87.9%. Results vary by workload.

Common questions.

What does tiers give engineering teams?
tiers gives engineering teams one governed endpoint across frontier, specialist, and customer-controlled intelligence. Each call clears its quality bar, is inspected for policy compliance, verified before the output reaches the application, and settled with a signed receipt. Teams add tiers by changing the base_url of the existing SDK client and retain their own provider keys and contracts.
How does tiers integrate with the SDKs our engineering team already uses?
tiers exposes an OpenAI-compatible endpoint and an Anthropic-compatible endpoint. Integration requires changing one value: set base_url in the existing SDK client to the tiers endpoint and replace the provider API key with a tiers key. Prompts, application logic, and provider relationships stay the same. The same approach works for direct HTTP calls.
Does adding tiers to the call path increase latency?
tiers adds less than one millisecond on the reference path. Decisioning, policy inspection, and verification run in-process within the governed call. The endpoint can be configured to fail open or closed by policy, so the overhead is bounded by design.
How does tiers help finance teams track AI spend?
tiers produces a signed receipt for every governed call, connecting each call to a team, deployment, task, model, provider, list-price cost, and governed cost. Per-session, per-team, and per-deployment budgets can be set to trip before a runaway loop becomes an invoice. The result is cost attribution at the call level without additional instrumentation.
How does tiers prove the savings it claims?
tiers produces a signed counterfactual that measures the identical workload governed and ungoverned in parallel, then signs both cost streams call by call. The counterfactual is specific to each customer's own traffic, not a generic benchmark. On the tiers reference workload, the 14-day signed baseline showed $6.10 per day governed versus $47.00 per day ungoverned, an 87% reduction at the maintained quality bar. Results vary by workload.
What does tiers give security teams for AI governance?
tiers inspects tool calls, HTTP requests, and subprocesses before execution, evaluating behavior across the full session rather than one prompt at a time. Intent and anomaly signals accumulate across the mission, so multi-step attacks that look harmless individually can be detected from their trajectory. Every blocked and allowed action is recorded in a signed receipt.
What audit evidence does tiers produce for compliance?
tiers produces a tamper-evident signed receipt for every governed call, containing the intelligence used, cost, policy decision, verification result, and a cryptographic signature. Receipts are hash-chained so any alteration to a historical record is detectable. They are exportable and form the audit trail for compliance reviews, FinOps attribution, and regulated deployment sign-off.
How does tiers help AI leaders manage a multi-model strategy?
tiers organises inference into 32 task-and-complexity cells. Each cell has a production occupant (the cheapest model that cleared the quality bar) and a shadow challenger being evaluated against real traffic. AI leaders define the task taxonomy and quality bars once; tiers continuously evaluates the market, promotes cheaper challengers when they clear the bar, and rolls back when quality drifts. The strategy becomes portable intelligence rather than provider-specific configuration.
How does tiers stay neutral across model providers?
tiers operates no models and sells no tokens. Its fee is 2% of governed spend, so it does not benefit from using a more expensive intelligence source. Frontier, specialist, and customer-controlled systems are evaluated against the same quality bar.
What does tiers give executives overseeing an AI program?
tiers gives executives one governed record for cost, quality, security, and evidence across every intelligence source and team. Each AI call is inspected, verified, and settled under one operating model, producing a measurable answer for AI economics, exposure, and control coverage. Every verified outcome makes the program more efficient and more specific to the company.
How does tiers support board-level AI governance reporting?
Every governed call produces a signed receipt that is exportable and hash-chained. Spend is attributable per team, deployment, and task. Policy intervention counts, verification pass rates, and counterfactual savings are measured continuously. Executives can answer questions about AI cost, quality, and risk exposure from the same evidence the operational teams use, with no separate reconstruction step.

1. Cost figures verified against a signed, immutable 14-day counterfactual: $6.10 per day governed versus $47.00 per day ungoverned on the reference workload; platform average 83.08% across cohorts, optimized cohorts 87.9%. The terminal session is simulated with reference-workload figures. Results vary by workload.