AI SpeedForce
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01Run

Agent engineering studio

AI agents that do real work in the systems you already run

We build task-scoped and multi-step agents that reach your stack through tools and MCP servers, act on real records, and hand off to a person when the decision needs one.

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Where your agents runOne agent core, with evals, tracing and human gates, working inside Shopify, Wix, WordPress, Ecwid, BigCommerce, Magento, Squarespace, Webflow, HighLevel, HubSpot, Salesforce, Zendesk, Custom stack.WHERE YOUR AGENTS RUNAgent coreevals and gatesShopifyWixWordPressEcwidBigCommerceMagentoSquarespaceWebflowHighLevelHubSpotSalesforceZendeskCustom stack
run · support triage run complete
  1. Trigger 0 ms A ticket lands in the support queue, tagged as a refund request.
  2. Plan 310 ms The agent reads the ticket, picks the three tools it needs, and orders the calls.
  3. Tool calls 3 in parallel · 204 ms Reads the order, the product record and the refund policy. No writes yet.
    1. orders.lookuporder #4471128 ms
    2. catalog.searchvariant match204 ms
    3. policy.retrievereturns, 30 day96 ms
  4. Check 88 ms The draft is tested against the refund rules and the eval cases for this task.
  5. Human approval approved Refunds over the threshold you set wait for a person. This one cleared in the queue.
  6. Result 1,180 tokens Refund issued, the order annotated, and the customer answered in your voice.
6 steps · 3 tool calls · 1 human gate
One agent run, trigger to result. An illustration of a run shape, not a client record.

02Build

Three lines of work, one point of accountability

Most jobs need two of the three. We scope which, and say plainly when a task does not need an agent at all.

Agent loopGoal then Plan then Tools then Check then Done, looping back until doneAGENT LOOPGoalPlanToolsCheckDone Agent engineering Agents that finish a task Task-scoped and multi-step agents that plan, call tools and know when to stop. tool use MCP servers memory and state Agent engineering RetrievalSources then Chunks then Search then CitedRETRIEVALSourcesChunksSearchCited Retrieval and data Answers grounded in your content Ingestion, chunking and embeddings, with retrieval quality measured rather than assumed. RAG pipelines ingestion retrieval evals Retrieval and data One runTrigger then Job then Approve then DoneONE RUNTriggerJobApproveDone Automation and workflows Work that runs without a tab open Triggers, background jobs and approval steps, with a path for what to do when something fails. human in the loop background jobs escalation Automation and workflows

03Trust

Reliability is the whole job

A demo agent works once. A production agent works on the hundredth run, on the input nobody planned for, at a cost you agreed in advance. That gap is where most of our engineering time goes.

Eval pass rate by buildIllustration. Share of eval cases passing across eight builds: build 1 88.0%, build 2 92.1%, build 3 95.6%, build 4 96.4%, build 5 92.3%, build 6 96.8%, build 7 97.5%, build 8 98.2%. Builds below the 95% ship gate do not ship; build 5 was a regression and was blocked.85%90%95%100%SHIP GATE 95%Build 1: 88.0% of eval cases pass, in developmentB1Build 2: 92.1% of eval cases pass, in developmentB2Build 3: 95.6% of eval cases pass, shippedB3Build 4: 96.4% of eval cases pass, shippedB4Build 5: 92.3% of eval cases pass, blocked: regression caughtB5Build 6: 96.8% of eval cases pass, shippedB6Build 7: 97.5% of eval cases pass, shippedB7Build 8: 98.2% of eval cases pass, shippedB8blockedILLUSTRATION, NOT CLIENT DATA
Evals

We write the test cases before the agent, from real examples you give us, and run them on every change. A regression shows up in the suite, not in your inbox.

One traced run, span by spanIllustration. Spans of one agent run in milliseconds: plan 0-310, orders.lookup 310-438, catalog.search 310-514, policy.retrieve 310-406, check 514-602, human approval 602-1320, result 1320-1400. Three tool calls run in parallel; the write waits at the human approval step.0 ms500 ms1000 ms1500 msplanplan: 0 to 310 ms (310 ms)orders.lookuporders.lookup: 310 to 438 ms (128 ms)catalog.searchcatalog.search: 310 to 514 ms (204 ms)policy.retrievepolicy.retrieve: 310 to 406 ms (96 ms)checkcheck: 514 to 602 ms (88 ms)human approvalhuman approval: 602 to 1320 ms (718 ms), waiting for a personresultresult: 1320 to 1400 ms (80 ms)waits for a personILLUSTRATION, NOT CLIENT DATA
Tracing and observability

Every run records its plan, tool calls, inputs, outputs and cost. When someone asks why the agent did that, the trace answers it.

Proposed write actions per dayIllustration. Write actions an agent proposed each day, split into executed, held for a person, and blocked by a rule: Mon 38 executed, 6 held, 2 blocked; Tue 42 executed, 8 held, 1 blocked; Wed 35 executed, 5 held, 3 blocked; Thu 47 executed, 9 held, 2 blocked; Fri 44 executed, 7 held, 1 blocked; Sat 20 executed, 3 held, 0 blocked; Sun 16 executed, 2 held, 1 blocked.0204060executedheld for a personblocked by a ruleMon: 38 executedMon: 6 held for a personMon: 2 blocked by a ruleMonTue: 42 executedTue: 8 held for a personTue: 1 blocked by a ruleTueWed: 35 executedWed: 5 held for a personWed: 3 blocked by a ruleWedThu: 47 executedThu: 9 held for a personThu: 2 blocked by a ruleThuFri: 44 executedFri: 7 held for a personFri: 1 blocked by a ruleFriSat: 20 executedSat: 3 held for a personSatSun: 16 executedSun: 2 held for a personSun: 1 blocked by a ruleSunILLUSTRATION, NOT CLIENT DATA
Guardrails

Write actions are checked against your rules before they execute, and an agent only ever holds the tools its task needs.

Cost per run against the agreed ceilingIllustration. Cost of each of 24 runs as a share of the agreed per-run ceiling, all below 100%. At run 16 a provider outage switches the agent to its defined fallback path. Values: 46%, 52%, 49%, 58%, 55%, 61%, 57%, 64%, 60%, 66%, 63%, 71%, 68%, 74%, 79%, 31%, 34%, 38%, 36%, 41%, 44%, 47%, 45%, 50%.0%50%100%CEILING (AGREED)fallback pathRun 1: 46% of the cost ceilingRun 2: 52% of the cost ceilingRun 3: 49% of the cost ceilingRun 4: 58% of the cost ceilingRun 5: 55% of the cost ceilingRun 6: 61% of the cost ceilingRun 7: 57% of the cost ceilingRun 8: 64% of the cost ceilingRun 9: 60% of the cost ceilingRun 10: 66% of the cost ceilingRun 11: 63% of the cost ceilingRun 12: 71% of the cost ceilingRun 13: 68% of the cost ceilingRun 14: 74% of the cost ceilingRun 15: 79% of the cost ceilingRun 16: 31% of the cost ceiling (provider down, fallback model)Run 17: 34% of the cost ceilingRun 18: 38% of the cost ceilingRun 19: 36% of the cost ceilingRun 20: 41% of the cost ceilingRun 21: 44% of the cost ceilingRun 22: 47% of the cost ceilingRun 23: 45% of the cost ceilingRun 24: 50% of the cost ceilingrun 1run 12run 24ILLUSTRATION, NOT CLIENT DATA
Budgets and fallbacks

Cost and latency ceilings are set per task and enforced in the run. When a model or an API is down, the agent degrades to a defined path instead of guessing.

What each agent type touches

Read access is broad, write access is narrow, and every write has a gate until you decide otherwise.

Agent Reads Writes Human gate
Support triage Tickets, orders, policy docs ticket.reply, refund.create Refunds over your threshold
Catalogue enrichment Product records, supplier feeds, images product.update, metafield.set Price and title changes
Order operations Orders, fulfilments, carrier status fulfilment.create, note.add Cancellations, address edits
Lead qualification Form fills, CRM records, call notes contact.tag, pipeline.move Outbound sends
Content operations Briefs, existing pages, brand rules draft.create Anything that publishes

04Ship

Agents inside the platforms you already run

Commerce stores, CRMs, support desks or your own stack. The same agent work, built against each system's own APIs, so it acts on live records rather than a copy of them.

Commerce

Shopify Support triage, catalogue enrichment and order ops against the Admin API. Shopify agents Wix Velo-side agents for lead capture, content ops and store answers. Wix agents WordPress Plugin-hosted agents for content operations and WooCommerce order work. WordPress agents Ecwid Embedded-store agents for product data and buyer questions. Ecwid agents BigCommerce Merchandising and order operations across large catalogues. BigCommerce agents Magento Catalogue, order and support agents on the REST and GraphQL APIs. Magento agents Squarespace Order, inventory and product agents on the Commerce APIs. Squarespace agents Webflow CMS drafts, form triage and order work on the Data API. Webflow agents

CRM and SaaS

HighLevel Lead qualification, pipeline moves and follow-up that a human signs off. HighLevel agents HubSpot Lead, ticket and deal agents on the HubSpot CRM API. HubSpot agents Salesforce Case, lead and opportunity agents on the REST API and Flow. Salesforce agents Zendesk Ticket triage and drafted replies on the Ticketing API. Zendesk agents

Custom

Custom stack Agents on your own APIs, databases and internal tools. Custom stack agents

Also here when you need them: web development and SEO, as supporting work around the agent build.

05Terms

What we commit to

Not results we cannot show you yet. These are the conditions we work under, on every engagement.

Evals Every agent ships with an eval suite Written from your real examples, before the agent exists.
Tracing Every run is traced end to end Plan, tools, inputs, outputs and cost, from the first day you use it.
Human gate Write actions wait for a person by default We open that up only where you tell us to.
Budgets Cost and latency ceilings are agreed before we build Enforced inside the run, not reviewed after the invoice.

06Check

Is your site ready for AI agents?

Assistants and agents now read, compare and act on websites for people. Our free scan checks whether yours gives them what they look for, and says how to fix each gap.

Scan your site What it checks
Readiness reportA readiness report: a score gauge beside a list of checks marked pass, partly or missing.READINESS REPORTSCOREAgent-ready?robots.txtllms.txtMarkdownLink headersAPI catalogAgent skillsMCP card22 CHECKS, EACH WITH A FIX
Checks 22 checks in six groups From robots.txt and llms.txt to API catalogs, skills and MCP.
Score A score out of 100 Core checks count double. Checks that do not apply are not held against you.
Fixes Every gap comes with a fix What is missing, why it matters and the standard it comes from.
Access Free, no sign-up Agents can call it too, as a JSON API.

07Read

From the blog

Plain guides to agentic browsing, answer engines and SEO, and what to change on your own site.

SEO

robots.txt for AI crawlers: GPTBot, ClaudeBot and the rest

AI providers run separate crawlers for training, for search and for fetching a page a user asked about. robots.txt lets you treat each one differently. Here is who is who and how to write the rules.Read the postSEO

llms.txt explained: what it is and how to write one

llms.txt is a short Markdown file at your site root that tells language models what your site is and where the important pages are. It is a proposal, not a standard, and it is cheap to publish.Read the postAEO

What is answer engine optimization (AEO)?

Answer engine optimization is the work of making your content easy for AI assistants to find, understand and quote. Here is how answer engines pick passages and what you control.Read the post
All posts

08Asked

Questions we get asked first

What counts as an agent, as opposed to an automation?

An automation follows a fixed path you wrote. An agent is given a goal and a set of tools, then decides which tools to call and in what order to reach it. We build both, and we say which one a job needs. Plenty of work that gets pitched as an agent is better and cheaper as a scripted workflow.

How do you stop an agent doing something wrong?

Four ways, layered. Scope: an agent only gets the tools it needs. Guardrails: write actions are checked against your rules before they run. Human gates: actions above a threshold you set wait for a person. Evals: we write test cases before the agent and run them on every change, so a regression shows up before it ships.

Can agents work inside our existing stack?

That is the point of the work. Agents reach your systems through tools and MCP servers we build against your APIs, so they read and write real records in Shopify, WordPress, HighLevel or whatever you run. Nothing has to move.

What does an engagement look like?

We start by scoping one task narrowly enough to be measured, agree the eval cases and the cost and latency budget for it, then build to those. You get the traces from day one, so you can see what the agent did and why before you widen its scope.

Do you still build websites and do SEO?

Yes, as supporting work. If an agent needs a front end, a storefront or content that ranks, we build it. It is no longer what we lead with. See web development and SEO.

Tell us the task, not the technology

Describe one job you would hand to a capable new starter. We will tell you whether an agent should do it, what it would touch, and what it would cost to run.

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