01
Audit
Two weeks mapping your workflows. We cost each one and rank them by what you'd actually save. You get a backlog ordered by money, not hype.
Ship agents that work.
Agents that actually run in production — ops, sales, support, code review. Human-approved actions, every decision traceable. Cheaper than the meeting about whether to build them.
Watch a live agent runDrops into the stack you already run
Postgres
Stripe
Gmail
Slack
Notion
GitHub
HubSpot
Shopify
Zendesk
OpenAI
Anthropic
Datadog
AWS
Postgres
Stripe
Gmail
Slack
Notion
GitHub
HubSpot
Shopify
Zendesk
OpenAI
Anthropic
Datadog
AWS
— What we do
Building agents for teams that
measure what they ship.
How it works
The Aegis Loop
Most agent projects stall in pilot purgatory: endless demos, nothing that ships. The Aegis Loop is how we get past that. Three steps, and your team owns what we build.
02
Ship
The top workflow goes live in four weeks. Real code in your stack, behind a feature flag, with a kill switch and a dashboard from day one.
03
Hand off
Your team gets the playbook, the evals, and the runbook. The next workflow ships without us on the call.
Live · multi-agent run
Watch the agents work, node by node.
p95 2.1s·cost/run $0.011·0.96 faith.
workflow · order_exceptions · prod-us-east-1
streaming
Trigger
queue · order_exceptions
Orchestrator
plan · route · supervise
Research agent
db.query(exceptions)
Action agent
stripe.refund($28.90)
Comms agent
gmail.draft(apology_v3)
Human approval
review · 1 click
Resolved
awaiting run…
›agent idle · waiting for the next exception…
Tool calls 0000Runs closed 00
Real trace from a client's order-exception queue, names scrubbed. Every consequential action passes a human gate.
Run this on your workflow →Strategy x Execution
01 — Map the workflow
Before any code, we score the candidate workflow on three axes: dollar value, how tractable it is, and how badly it can fail. The eval rubric gets written before the first prompt.
02 — Ship the loop
One agent in production behind a feature flag. Real traffic, kill switch wired up. Faithfulness, latency, cost, escalation rate — all on a dashboard your CFO will actually open. Boring on purpose.
What we ship
View all services →Proof
Shipped work, measured in hours and dollars.
Insurance / Back office
Claims processed 40% faster with a human-gated agent
An agent assembles each claim file, checks it against policy rules, and queues a recommended decision. Humans approve; nothing pays out on its own.
40%
faster claims cycle
−22%
rework rate
100%
decisions human-approved
SaaS / Sales
3x qualified pipeline from a research crew of agents
A multi-agent crew researches every signup, scores fit, and hands SDRs a briefing instead of a raw lead list. Pipeline tripled without growing the team.
3x
qualified pipeline
35
hrs/week of manual research
400+
leads enriched daily
Logistics
Freight quotes turned around 70% faster
An intake agent reads inbound quote requests from email and EDI, extracts lanes and constraints, and drafts the quote before a human ever opens the thread.
70%
faster quote turnaround
92%
requests auto-parsed
2.4x
more quotes/day
SaaS
Pipeline up 30% without a new hire
Built an agent that researches accounts, drafts outreach, and hands warm context to reps before every call.
+30%
qualified pipeline
−40%
rep ramp time
3.2×
outbound reply rate
0hrs/wk
saved per operator
0%
lift in qualified leads
0%
revenue growth
0%
faster turnaround
0%
lower cost-to-serve
Representative outcomes from recent engagements
In their words
The payback window was ten days. We had four ops hires on the hiring plan and cut it to one.
Director of Operations
Mid-market retailer
They shipped something working in week one. Not a slide deck, not a demo — something we use every day.
Head of Growth
B2B SaaS, Series B
Our reps now close deals with context they never had. Pipeline is up 30% without a single new hire.
VP Sales
Industrial services
Our Approach
Pick one workflow that's costing you. We build the agent. Your team owns it. Done in weeks.
Agentic Pulse · refreshed daily
What's moving in agentic AI.
Auto-curated from arXiv, Hacker News, OpenAI, and Hugging Face — the papers and launches that change how multi-agent systems get built.
Hugging FaceFrom the Hugging Face Hub to robot hardware with Strands Agents and LeRobot3h agoarXivIntermittent Strategic Cooperation of Two Selfish Agents on Graphs10h agoarXivTrustworthy Self-Composable Big-Data-as-a-Service: An LLM-Orchestrated Multi-Agent Framework for Automated Data Engineering, AutoML, MLOps Deployment, and Drift-Aware Lifecycle Optimization10h agoHNPonytail – make your AI agent think like the laziest senior dev in the room2d agoHNAI agent bankrupted their operator while trying to scan DN425d agoHugging FaceHow an Agent Built a 3D Paris Gallery by Chaining Two Hugging Face Spaces8d ago
This is the firehose we drink from before we ship anything for a client.Ask us what it means for your ops →
Engagement model
Start with a pilot, not a contract.
No tier menus, no retainers up front. One scoped workflow, shipped in weeks, with measurable ROI before any bigger commitment. If it doesn't pay for itself, we stop, and you keep everything.
Weeks 1–2
Audit
We map your workflows and pick the one where an agent pays for itself fastest. Target ROI goes in writing.
Weeks 3–6
Pilot
One agent, shipped to production with human-in-the-loop approvals, traces, and evals on every run.
Then
You own it
Code in your repos, models in your cloud. Scale up only after the pilot has already proven the number.
Quick math
What could an agent give back?
Drag the sliders for one repetitive workflow your team runs today. The estimate is deliberately conservative; we pin the real number in the audit.
4
10 hrs
$45
Hours reclaimed / year
1,248
Estimated savings / year
$56,160
Estimate at ~60% of the task handled by an agent. Your real target gets scoped and put in writing during the audit.
Questions
Asked on every first call.
How does an engagement start?
With a call and then a pilot: one workflow, scoped in writing, shipped in weeks. No retainers, no tier menus — you see measurable impact on a real workflow before any bigger commitment.
How fast do we see value?
The first workflow has to pay for itself in ten working days or we pause and pick a better target. That's a hard rule, not a marketing line.
How do you keep agents under control?
Every agent ships with human-in-the-loop approval on consequential actions, full trace logging, and evals scored on every run. You can see every decision the agent made and why — nothing runs dark.
What if the pilot doesn't work?
Then we stop, and you keep everything we built plus the workflow map from the audit. The pilot is designed so the downside is a few weeks, not a contract.
Who owns the code and models?
You do, from commit one. Everything ships into your repos, your cloud accounts, your observability stack.
Do you sign an SOW or a retainer?
Fixed-scope SOW for pilots, strategy sprints, and one-off builds. Ongoing engagements only after a pilot has already proven the ROI.
What stacks do you work in?
Python, TypeScript, and whatever your team already runs. We're pragmatic about tooling — the goal is your team owning the result, not a greenfield.
Booking 4 slots this week · 2 left
One workflow.
A 60-min call.
An agent your team owns.
Bring the workflow that's hurting most. By the end of the hour you'll have a build-or-buy decision, a target cost-per-run, and a date on the calendar.
60 minutes. No sales deck.
Build/buy call and target cost-per-run, in writing
Eval rubric drafted live on your messiest workflow
NDA back in under 24 hours
SOC 2 Type IIISO 27001GDPRHIPAA-eligibleFrom $40K