The intelligence layer over your existing operations
Six modules over one connected data layer — dashboards, plain-language answers, ML forecasting, anomaly alerts, recommended actions and automatic reports. All running today.
Your systems 5 sources · 15,738 rows
-
“Acme Corp”CRM
-
“ACME Corporation”Invoicing
-
“acme corp.”Spreadsheets
-
Support desk
One customer. Three different spellings.
Why did revenue dip in March?
AnswerDown 12% in March — almost all of it fewer repeat orders in the Northwest.
From Orders + Invoicing · 4,182 rows — every figure traceable to its source.
The same customer, spelled three ways across three systems — matched into one record, so the answer on the right can be trusted.
Most businesses aren't short on data. They're short on answers.
Spreadsheets, CRMs, invoicing tools, support tickets — the data exists. It's just scattered, inconsistent, and locked behind whoever knows how to query it.
The cost shows up quietly: decisions get made three weeks late, on last month's numbers. Deals slip because nobody could confirm a number fast enough. The one person who "knows the spreadsheet" becomes a bottleneck for every question the rest of the team has.
Data lives in silos
Every tool exports data differently. Nobody has time to reconcile it, so decisions get made on gut feel or last month's export, not on what's actually happening now.
- Reports take days to assemble, not minutes
- Only one or two people can "run the numbers"
- Insight requires a technical middleman
One connected layer over your data
SolmiriX connects to the systems you already use, uses AI to map and clean the mess into a structured layer, and lets anyone on your team get AI-powered analytics in plain language.
- No rip-and-replace of your existing tools
- Non-technical staff can query directly
- One source of truth instead of five exports
The intelligence layer on top of your existing operations
SolmiriX is built on the same descriptive-to-prescriptive analytics maturity model analysts use to benchmark a company's data capability — four stages, each one usable on its own, more powerful together.
-
Descriptive
What happened?
Standard dashboards and reports, across every system at once.
Data Unification Engine
-
Diagnostic
Why did it happen?
Root-cause analysis and drill-downs, in plain language.
Ask AI
-
Predictive
What’s likely next?
ML forecasting and anomaly detection, up to 90 days out.
Forecasting · Anomaly Detection
-
Prescriptive
What should I do?
Recommended actions with the reasoning shown — always human-approved.
Recommendations · Auto-Reports
Five inconsistent exports. One structured layer.
Connects to the scattered systems you already run, then uses AI to auto-map fields, clean formatting mismatches, and remove duplicates — without you rebuilding your stack.
Count how many places one customer lives: the same company is almost certainly listed three different ways across your CRM, your invoicing tool and a spreadsheet — and somebody reconciles them by hand at month-end, from memory. What changes: the records are matched and merged automatically, and you can see which source each field came from.
- Connect
- Profile
- Clean
- Merge
- Serve
-
Connect
Read-only connections to the systems you already run — CRM, invoicing, inventory, the shared spreadsheets, your support desk. Nothing is migrated and nothing is replaced. Your tools carry on being the systems your team uses.
-
Profile
It works out what each column actually is.
cust_name,Customer Nameandacct_nmare recognised as the same thing, types are inferred, and the empty and malformed values get counted before anyone builds a number on top of them. -
Clean
Formats are normalised so the same fact is written the same way everywhere — dates, currency, casing, phone formats. Rows that are genuinely junk are quarantined where you can inspect them rather than silently dropped.
-
Merge
Records that describe the same customer collapse into one, with a lineage trail showing which source each field came from. When two sources disagree, you decide which one wins — and the losing value is kept, not erased.
-
Serve
The result is one structured layer that everything else reads from: Ask AI, your dashboards, exports, and the forecasting modules further down this page. It refreshes on a schedule, so it does not go stale the week after it is built.
- Auto-schema & field mapping
- Deduplication & cleaning rules you control
- Connectors for the tools you already run
- One structured source of truth, with lineage
Sources 5
- CRM
- Invoicing
- Inventory
- Spreadsheets
- Support desk
Step 1 · Connect
- AccessRead-only ✓
- Migration neededNone ✓
- WorkspaceIsolated ✓
- Total rows15,738
Nothing is moved or replaced. Your systems stay exactly where they are.
Step 2 · Profile
- customercust_name
- customerCustomer Name
- customeracct_nm
- ⚠region
Three names, one thing. The gaps get counted before anyone builds a number on top of them.
Step 3 · Clean
- 03/04/26 · 2026-04-03One format ✓
- "NPR 64,200"64200.00 ✓
- acme corp. · ACMEAcme Corp ✓
- 214 junk rowsQuarantined ⚠
Quarantined, not deleted — you can open the pile and check what it decided.
Step 4 · Merge
- Acme Corp3 records → 1
- Emailfrom Invoicing ✓
- Regionfrom CRM ✓
- Phone conflictCRM wins · kept in history
Every field remembers where it came from. You can always ask why a figure says what it says.
Step 5 · Serve
- Ask AIPlain-language questions ✓
- DashboardsStandard reporting ✓
- ForecastingPredictive modules ✓
- ExportExcel, PDF or API
One layer, refreshed on a schedule — so it does not go stale the week after it is built.
Ask a question. Get an answer, not a chart.
Type a question the way you'd ask a colleague, and get a clear answer back — with the numbers it's based on shown alongside it, not hidden behind a chart nobody can query further.
Think about the last time you asked for a number: the question was one sentence, and getting it answered took a request, a queue, and somebody's afternoon — by which point you had partly stopped caring. What changes: you type the sentence and get the answer, along with the rows it came from, so you can check it yourself.
- Ask
- Plan
- Look
- Answer
- Trace
-
Ask
A question in ordinary words, in English or Nepali. No query language, no field names, no waiting for the one person who knows how the spreadsheet is put together to come back from leave.
-
Plan
Before it looks anything up, it says what it thinks you are asking for — which measures, which period, which breakdown. You get to catch a misunderstanding here, at the cheap end, instead of arguing with a wrong number later.
-
Look
It reads from the unified layer, and only the parts you are permitted to see. Which sources it touched is shown, so an answer is never a black box — and someone who cannot see all regions does not get an answer that silently includes them.
-
Answer
One plain sentence carrying the actual number, with a small chart underneath for shape rather than instead of an answer. The point is that you can read it out in a meeting without translating it first.
-
Trace
Every figure links back to the rows behind it. Export it, or ask the obvious follow-up and keep going. An answer you cannot check is an answer you should not act on.
- Natural-language querying
- English + local-language support
- Source-transparent, permission-aware answers
- No SQL or analyst required
Step 1 · Ask
Why did revenue dip in March?
11:02 AMAn ordinary sentence — no query syntax, no field names. Nepali works the same way, and the answer comes back in the language you asked in.
Step 2 · Plan
- MeasureRevenue, monthly
- PeriodJan – Mar 2026
- Break down byRegion
- CompareRepeat vs new
It says what it thinks you meant before it looks anything up — so a misunderstanding costs you one click, not one meeting.
Step 3 · Look
- ✓Orders
- ✓Customers
- ✓Invoicing
- Permission check
Named sources, and only what you are allowed to see.
Step 4 · Answer
Revenue fell 12% in March. Almost all of it is fewer repeat orders in the Northwest — new customers were flat, and no other region moved.
11:02 AMके यो वैशाखमा पनि जारी छ?
11:03 AMछैन — वैशाखको दोस्रो हप्तादेखि उत्तर-पश्चिममा दोहोरिने अर्डर सामान्य स्तरमा फर्किएको छ।
11:03 AMAsked in English, answered in English. Asked in Nepali, answered in Nepali — the follow-up above switched language and the reply followed it.
Step 5 · Trace
- The 12%Open the 4,182 rows behind it ✓
- ExportExcel, PDF or API ✓
- Follow up"Which customers stopped ordering?"
- ShareSend the answer with its sources
An answer you cannot check is an answer you should not act on.
Four modules, running in production today
Forecasting, anomaly detection, recommendations and auto-reports — all four are released and running, not roadmap. Pick one to see how it works.
Unify → Ask → Forecast → Act
Predictive Live nowPredictive Forecasting Engine
once your data is unified, statistical and ML models forecast what's coming — demand, revenue, cash flow — instead of everyone relying on last month's numbers and gut feel.
For example: forecasting next quarter's revenue from historical trends and current pipeline, flagged 90 days out instead of discovered after the quarter closes.
- Demand, revenue & cash-flow forecasts
- 90-day-ahead risk alerts
- Confidence ranges, not false precision
- Retrains as your history accumulates
How the Predictive Forecasting Engine works
Next quarter +12% projected, flagged 90 days out
- Learn — the model trains on your historical trends and current pipeline.
- Forecast — demand, revenue, or cash flow projected forward.
- Flag — risk is surfaced up to 90 days before it hits.
- Refine — the model improves with each new engagement.
Unify → Ask → Detect → Act
Predictive Live nowIntelligent Anomaly Detection
watches your unified data continuously and flags what's statistically off — a metric moving outside its normal range — and routes the alert to the right person immediately, instead of someone noticing it weeks later in a report.
For example: a sudden drop in one region's order volume gets flagged and routed the same day it happens, rather than surfacing in next month's review.
- Statistical & predictive alerting
- Real-time routing to the right owner
- Tunable sensitivity per metric
- Learns your normal, so alerts stop being noise
How Intelligent Anomaly Detection works
Order volume dropped 18% — alert routed to Ops
- Watch — every key metric is monitored continuously.
- Detect — AI flags a statistically significant deviation.
- Alert — the right owner is notified the same day it happens.
- Tune — sensitivity is adjusted per metric, not one-size-fits-all.
Unify → Ask → Forecast → Recommend
Prescriptive Live nowPrescriptive Recommendation Engine
goes one step past forecasting and anomaly alerts to suggest the specific action to take — a resource shift, a process change — with every suggestion routed for human approval before anything runs.
For example: flagging that shifting staff scheduling in one location would recover a projected shortfall, with the reasoning shown, instead of just reporting that the shortfall exists.
- Enrollment/resource-style action suggestions
- Reasoning shown alongside every suggestion
- Human-approved before anything executes
- Every suggestion logged with what happened next
How the Prescriptive Recommendation Engine works
Shift 2 staff from Location A to Location B to cover Friday's projected shortfall.
- Read — the engine reads the forecast or anomaly that triggered it.
- Suggest — a specific action is proposed, with reasoning shown.
- Approve — a human reviews and signs off before anything runs.
- Learn — the model improves with each new engagement.
Unify → Ask → Forecast → Report
Prescriptive Live nowAuto-Report Generation
generates the narrative report someone currently assembles by hand every week — written in plain language, scheduled to arrive automatically, exportable to the format your stakeholders expect.
For example: a board-ready monthly summary that used to take a full day to compile now generates itself and lands in the right inbox on schedule.
- Narrative, plain-language reports
- Scheduled, board-ready delivery
- Excel / PDF / API export
- Your template and tone, not a generic layout
How Auto-Report Generation works
- Refresh — the report pulls the latest state of your unified data.
- Draft — AI writes the narrative summary, not just a table.
- Deliver — it lands in the right inbox on schedule, board-ready.
- Export — download as Excel or PDF, or pull it via API.
Industries where scattered data costs real time and money
SolmiriX is industry-agnostic at its core — the same unify → ask → forecast → act pattern applies wherever data lives in disconnected systems.
What "live" means here
All six modules are running today. What varies between businesses is not the software but the data: how many systems you have, how consistent they are, and how much cleaning the first pass needs. A straightforward setup connects in days; a tangled one takes longer, and we will say which yours is after we have looked.
We would rather show you SolmiriX reading your data than demo it on ours. If it turns out the platform is not the right answer for your problem, we will tell you that too — Professional Services can automate or connect your systems directly without it.