YOUR AI BILL IS MOSTLY REPEAT WORK

Frontier quality on your recurring work, at a fraction of the cost.

Our self-optimizing agent runtime plugs into the Claude and Codex your teams already use. It takes the recurring jobs, runs them, and keeps finding cheaper ways to get the same work done. Nothing changes until it beats your quality bar on real cases.

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Cost and quality across promoted playbooks

OWN THE DATA LOOP

You are not buying a tool.
You are building something your firm owns.

THE ASSET IN THE MIDDLE Your playbook library

38PLAYBOOKS

214PROVEN VERSIONS

Every completed lap leaves something behind. That residue is the moat, and it stays inside your perimeter.

01OBSERVE

Every run traced Prompts, tool calls, model choice, cost and latency, captured inside your perimeter.

02EVALUATE

Scored against your bar Your rubrics and graders decide what good means. Not a vendor default.

03OPTIMIZE

A cheaper way, proposed The runtime tests instructions, tools, models and structure against the baseline.

04PROMOTE PLAYBOOKS

One approval, every team An admin deploys the winner. Authorized teams inherit it on their next run.

05EVOLVE

The library compounds Promoted playbooks keep being scored, so the next candidate starts higher.

STAGES 01 TO 03Observability and evals, which a well-funded category already sells to engineering teams.

STAGE 04Promotion turns one team's win into firm policy. It needs an approval chain, not a pull request.

STAGE 05Evolution means the library is worth more next quarter than today, without anyone maintaining it.

EVALS · OBSERVABILITY · SELF-OPTIMIZING RUNTIME

Your firm's judgment, absorbed at scale and never handed out.

Our AI graders judge every run against your rubrics, the runtime routes each task shape to the cheapest mix that still clears your bar, and the saving comes back to you as a playbook your admins approve once.

Optimization Every run scored for cost and against your quality bar

4,182 runs · 100% scored · 30d Candidates History

                  Task and model mixRunsQuality vs barCost / runVerdict
                

Client portfolio reviewGLM 5.3 Flash · Opus 5 · DeepSeek 4.1 Flash 1,240 +2.4 $1.18 CANDIDATE

Trace · candidate v15, run 1,240 4 steps · 38s · $0.41

01 Pull positions and benchmark series GLM 5.3 Flash $0.04

02 Attribute drift and flag breaches GLM 5.3 FlashSWAPPED $0.11

03 Draft client-ready narrative Opus 5 $0.22

04 Check citations and disclosures DeepSeek 4.1 FlashSWAPPED $0.04

Tax season deskGLM 5.3 Flash · DeepSeek 4.1 Flash 1,104 +1.2 $0.88 CANDIDATE

Suitability memo draftingGLM 5.3 Flash 906 +0.9 $0.64 DEPLOYED

Filing summary and citationsDeepSeek 4.1 Flash · GPT 5.6 Sol 1,517 -1.6 $0.39 HELD BACK

Onboarding diligence checklistGLM 5.3 Flash · Kimi K3 519 +1.7 $0.21 DEPLOYED

Nothing on this screen was configured by hand. Observability supplies the run, the eval suite supplies the verdict, the runtime supplies the candidate.

Review candidate 2 awaiting approval

Client Portfolio Review v14 → v15

Restructured into four steps, two of them moved off Opus 5 onto GLM 5.3 Flash and DeepSeek 4.1 Flash, retrieval cached across the book. Proposed by the runtime after 1,240 scored runs.

Cost per run $1.18 → $0.41

Quality vs bar +2.4 pts

                      Scorers · 148 unseen casesv14v15Δ
                    

Allocation accuracy 0.91 0.94 +0.03

Citation validity 0.88 0.96 +0.08

Disclosure completeness 1.00 1.00 HELD

House tone and format 0.86 0.82 -0.04

One regression, inside tolerance. Reviewer sample 20 of 148, experiment spend $84. CLEARS THE BAR

Promote to 6 ADVISORY TEAMS · 84 SEATS

Deploy enhancements Hold

Approved once by a human who is accountable. Inherited by every authorized team on their next run. Figures illustrative until the first design-partner measurement.

ABSORBED Every run teaches the runtime The corrections your experts make by hand today become the standard the system is measured against tomorrow.

OPERATIONALIZED One approval, whole enterprise Your best practitioner's method stops being folklore in one team and becomes the default everywhere.

CONTAINED It compounds for you, not a vendor Playbooks and eval records stay yours, inference runs on zero-retention providers, and none of it trains someone else's model.

THE MODEL LAYER

The best model for every task, chosen for you.

Betting your firm on one AI vendor is a concentration risk. Frontier spreads it: Claude, GPT and Gemini work alongside the strongest open models like GLM and DeepSeek as a mixture of agents, and no single provider can hold your roadmap or your pricing hostage. You don't need a team to keep up with the market. The platform tests each new model on your own work and adopts it only when it raises quality.

OpenAI

Grok

MCP

API

Legacy

MCP

MIXTURE OF AGENTS

Own your intelligence

Everything the runtime learns, every playbook, eval record and approved improvement, becomes an asset your firm holds, not one a vendor rents back to you. You decide where it runs, which models it uses, and who it answers to.

Own your compounding intelligence

Your playbooks and eval records, held by you and portable across any model. Never a vendor's next release.

Keep your data yours

Runs stay in your perimeter, your clients' data never leaves, and nothing you do trains someone else's model.

Host it anywhere

On-prem, your cloud, or fully local, wherever compliance requires.

No vendor lock-in

Any model, open or closed, swapped anytime. No vendor holds you hostage.

Use what you already pay for

Bring your Claude, GPT, or Gemini seats. No double-paying, no rip-and-replace.

Make it your own

Build on it, tailor it, white-label it as yours.

START WITH ONE RECURRING WORKFLOW

Bring us your most expensive hour

The platform instruments it automatically, scores every run against your bar, and surfaces the cheaper version before anything is promoted. You keep the playbook either way.

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