AI pointed at the work your team shouldn’t be doing.
AI put inside the workflows still running on manual effort — intake, triage, summarization, drafting, routing. Grounded in your data. Observable in production. Handed off to humans when it should be.
Agent · last 24h
Task ledger
Tasks run
142
last 24h
Auto-applied
87%
+4%
Needs review
19
human-in-loop
AI wired into the tools your team already uses. Not another browser tab.
Outputs grounded in your systems of record. Versioned, cited, auditable.
Cost and latency metered per prompt. ROI is never a guess.
How we build AI that holds up in production.
The same engineering discipline we bring to every other system — applied to the thing most teams are still shipping as a prototype.
Grounded in your data
LLMs wired to your systems of record — CRM, ledgers, document stores, wiki. Not the public internet. Every answer is versioned, cited, and auditable.
Response · cited from your data
v2 · versionedBlackArrow’s Q2 renewal total is $42,1801, a +8% increase2 over their Q1 invoice. The account is owned by Priya Sharma3, flagged as renewal-at-risk4 since Feb 18.
Observable in production
Every prompt, retrieval, and response logged with cost and latency. You see what the AI did, why, and what it cost — the same way you see any other piece of software.
Observability · last 4 calls
/metrics/agentCost · 24h
$3.18
↓ 12% vs yesterday
Hands off when it should
When the model is confident, it executes. When it isn’t, it routes to a reviewer with full context. You keep the boundary. The team keeps the final say.
Routing · confidence threshold
threshold · 90%Built for ROI, not demos
AI ships where the math works — where the hours saved or errors avoided pay for the system several times over. Everything else stays as plain software.
ROI · March
/reports/ai-roiNet saved
$12,480
288 hours returned to the team · March
Hours saved
across 6 workflows
AI run cost
142,000 requests
Net ROI
on AI spend
From workflow map to AI in production.
Short engagements that prove the numbers before they prove the model. If AI isn’t the right answer, we tell you.
Workflow mapping
We sit with the team doing the work. Map every step. Find the specific repetitive work AI can actually remove.
Build and ground
Retrieval, prompts, guardrails, fallbacks, and the cost and latency wiring. Built inside the systems your team already uses.
Rollout and measure
Shadow mode, then human review, then graduated autonomy. Dashboards show hours saved and dollars spent in real time.
Start with the workflow costing you the most hours.
Short engagements that prove the numbers first. If AI isn’t saving hours or dollars, we say so.