Weekly KPI brief
Summarize movers and why, with segment cuts and confidence notes, in the language your review already uses.
BigQuery · Sheets · Slack
Agent template · created in seconds
Your data agent.
Created in seconds.
Pull the numbers that matter, explain what changed, and keep reporting honest, without hiring, without another empty analyst seat, without paying $12,000/month for a BI tool nobody opens. One example of an agent you can create on Upchat in seconds, then customize freely: model, tone, tools.
Data Agent · running
DA
Data analyst
Running 3 tasks in parallel
9:04 · Tuesday
YouThis week: explain why activation dipped and prep the KPI brief.
Data AgentGot it. I've read your
→ Define, metric specs and guardrails, one source of truth
→ Draft, SQL validated, edge cases checked
→ Narrate, KPI brief with movers, why, and what to do
METRICS.md and last quarter's dashboards. 3-part plan:→ Define, metric specs and guardrails, one source of truth
→ Draft, SQL validated, edge cases checked
→ Narrate, KPI brief with movers, why, and what to do
Data AgentLaunching, first SQL pass and anomaly note in 3 hours.
While your competitors are still waiting on a data analyst, the agent you created this morning is already explaining the dip.
The problem
Work piles up where judgment and follow-through should meet.
You have metrics, a warehouse, and a leadership team that wants answers. But between you and a believable number, there's always the same bottleneck: SQL, exports, and a narrative someone actually trusts.
01 · QUEUE
Ad-hoc questions pile up, answers arrive after the decision.
Slack pings, last-minute asks, "just one chart." By the time you answer, the call already happened.
02 · TRUST
Three spreadsheets, three truths, leadership stops believing the charts.
$400-700/day, fractional, juggling three clients. Your numbers never quite match the source of truth.
03 · SPEED
SQL, exports, slides, a simple trend check burns half a day.
Query, validate, format, narrate. A trend check that should take 20 minutes eats the afternoon.
04 · CONTEXT
Metrics without narrative, teams still ask what to do next.
Stale dashboards, missing definitions, no owners. By the time the chart is clear, the moment passed.
Meanwhile, decisions get made on gut, and the dashboards lag the truth.
The solution
A dedicated data AI agent. It understands, proposes, executes.
Upchat is a data analyst on the job, from minute one.
It understands your metrics, your schema, and your stakeholders, then executes the next best query immediately. No 8-week ramp. No "I'll rebuild the dashboard tomorrow."
- 01 Define metrics & guardrails
- 02 Draft SQL & validate edges
- 03 Build KPI narratives
- 04 Spot anomalies and propose checks
No friction. No latency. No dependency on a human for every chart.
upchat ~ data-analyst
▸read metrics_and_schema.md
✓ metrics, definitions, 12 months of warehouse history ingested
▸propose plan --q=activation_dip_explain
→ 3 prioritized reads: anomaly · funnel · experiment readout
▸execute sql --warehouse=prod --validate-edges=true
⚡ 7 queries live · KPI brief drafted · 3 anomalies flagged
▸monitor
Why it's different
It's not a tool. It's an operator.
ChatGPT answers. A BI tool gives you charts. Upchat Data actually drafts your SQL and explains what changed, while you do something else.
a tool
an assistant
a copilot
→ an autonomous operator
5min
Ready in minutes
No endless setup. You brief, it starts.
24/7
Always on
It doesn't sleep, take PTO, or change jobs.
+∞
Learns your business
Every interaction enriches memory. It knows your metrics and segments better every day.
0s
Executes instantly
The gap between "what changed?" and "here's the SQL and the why" drops to near zero.
v∞
Always up to date
Continuous watch on your metrics and anomalies. No dip goes unnoticed for a week.
You go from "I'll pull it by Friday" to "the brief is already drafted".
How it works
Three steps. Zero friction.
You stay in control on what matters. The agent never ships a number alone, changes definitions, or approves forecasts. It proposes. You validate. It executes the motion.
01
~5 min
Describe your data
Metrics, schema, warehouse, and stakeholders. The agent ingests what matters to query and narrate the right way.
02
~1h
The agent proposes a plan
Metric specs, SQL drafts, anomaly checks, KPI briefs. A prioritized plan with expected signal and time to first readout.
03
ongoing
It executes and optimizes
Queries, narratives, anomaly watches, and fewer decisions made on gut, week after week.
What the agent can do
Everything a data analyst would do · but continuously.
Define metrics & guardrails
Metric specs, denominators, exclusions, and one source of truth, without letting definitions drift across teams.
Draft SQL & validate edges
Queries, edge cases, null handling, and schema-aware joins, in your warehouse, tied to the real question.
Build weekly KPI narratives
Movers, why, and what to do, with segment cuts and confidence notes, not a wall of numbers.
Spot anomalies & propose checks
Thresholds, cohort drops, and data-quality alerts. Surfaces the right signal before the call, not after.
Turn funnel drops into hypotheses
Step-by-step breakdowns, segment flags, and testable next steps, not a flat conversion rate.
Package charts with takeaways
Charts, plain-language readouts, and recommended actions. The story behind the number, ready to share.
Missions & first week
Metrics work agents can own
Less "pull me a chart," more KPI briefs, anomaly watches, and funnel reads that stay defined and in the right segment.
Starter missions
Funnel health check
Break down step-by-step conversion, flag cohort drops, and propose testable hypotheses for activation gaps.
BigQuery · Mixpanel-style events · Sheets
Experiment readout
Compute lift, significance, and segment splits, with a plain-language verdict and recommended next step.
BigQuery · Sheets · Notion
Data quality audit
Scan core events for nulls, schema drift, and tracking gaps, and flag breaks before they corrupt the dashboards.
BigQuery · GitHub · Slack
Your first week
Day 1
Metrics + schema snapshot
Connect the warehouse (or equivalent), define core metrics, denominators, and beacons of a clean event.
Day 2-3
Shadow two live asks
Agent drafts SQL and brief; you compare to how your best analyst would write it.
Day 4-5
Quality batch
Stale definitions, missing owners, tracking gaps - clean before it owns more lifecycle.
Ongoing
Metrics rituals
Monday KPI note + daily anomaly scan with publish approval still on you.
Early access - human approval stays on high-impact actions (forecast commits, definition changes, external publishes).
A team, not an individual
Your data agent doesn't work alone.
It collaborates with other specialized agents. A product question on the metric? The PM agent helps. A growth lever on the funnel? Growth brings the angle. A pipeline edge case? The dev agent digs in. A full team, without hiring anyone.
DA
Lead agent
Data Agent
Owns your metrics motion end to end. Defines, queries, narrates, documents, and calls the rest of the team when needed.
PM
Contributor
Product manager
Clarifies which metric matters, the decision behind it, and what good looks like.
← called by Data
GH
Contributor
Growth hacker
Connects funnel signal to experiments and where to double down on acquisition.
← called by Data
DV
Contributor
Developer
Digs into pipeline edge cases, event tracking, and schema drift behind the numbers.
← called by Data
Freelance vs. Upchat
Why manage someone when you can run a system?
Freelance analyst old school
- ✕ Slow to hire, 6 to 10 weeks
- ✕ Expensive, $6,000 to $12,000/month + variable
- ✕ Limited time, 2 days/week or quarter-only pushes
- ✕ Multiple clients, never the priority
- ✕ Vacations, job changes, context resets
- ✕ Back to zero when they leave
Upchat Data new
- ✓ Instant, ready in 5 minutes
- ✓ Pay-as-you-go, pay for what you use
- ✓ 24/7, nights, weekends, holidays
- ✓ 100% dedicated, one priority: you
- ✓ Never leaves, never forgets, always improves
- ✓ Your memory stays yours, forever
Stop managing a person. Start steering a system.
Keep learning
Guides for this role
What is Agentic RAG? A practical guide for teams
Agentic RAG explained: how AI agents plan, retrieve, and verify knowledge in loops, why classic RAG fails on complex work, and how teams apply it this week.
Read the guideHow to Evaluate AI Agents: A Practical Guide for Teams
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Read the guideWhat is AI agent memory?
AI agent memory explained: short-term context, long-term recall, team knowledge, and how shared memory turns one-off chats into reliable cloud agents.
Read the guideYour stack
Connect it to all your tools. Even the ones that don't exist anywhere else yet.
Ads, CRM, support, drive, internal tools. Upchat plugs into 3,000+ tools out of the box, and if the one you need isn't there yet, it finds the documentation, writes its own connector, and connects itself with the API key or credentials you give it.
- You name the tool, even one built in house.
- It reads the documentation and builds its own connector.
- You give it an API key or your credentials, it connects itself.
Connect to our internal tool, Ledgerly.
Upchat
No connector exists yet. I'm reading its API documentation and building one.
No connector exists yet. I'm reading its API documentation and building one.
Upchat
Ready. Give me an API key to finish the connection.
Ready. Give me an API key to finish the connection.
3,000+Ready integrations
∞Reachable tools
3,000+ integrations
This is just a preview, Upchat connects to 3,000+ tools
Early access
Launch your data agent before everyone else.
Upchat is in early access. We're opening spots gradually for the first users, prioritizing data teams that need honest reporting without the headcount wait. Get early access and create this agent in seconds, zero setup.
No commitment, priority access at launch.