We connect data, applications, and business rules so agents can prepare, verify, and execute operational work—with human gates, traceability, and measurable outcomes.
Start with one workflow, ten business days, and one agreed metric.
Enterprise operations where our founder worked.
We work with VP Operations, COOs, and Heads of Data at mid-market companies where volume grew faster than the systems behind it. We do not start by adding AI across the company: we look for a frequent flow with accessible data and an output your team can verify.
When a pipeline job fails at 2am, someone has to check logs across the orchestrator, the warehouse, and the ticketing system before they know what broke.
The agent correlates the failure across systems of record and proposes the fix or the rollback. An on-call engineer approves before anything runs in production.
Reconciling warehouse spend against budget means exporting billing data and stitching it to usage in a spreadsheet, and overruns only surface when the invoice lands.
The agent reconciles usage and billing data continuously against the business rules you define, and flags real variance before the invoice arrives. Finance and the data team approve the exceptions together.
A single upstream schema change breaks three BI dashboards, and nobody notices until a stakeholder asks why the numbers don't match.
The agent watches for schema drift and broken lineage, flags the affected dashboards, and drafts the fix. The data team reviews before it ships to production.
Every "why does this number look wrong" ticket means someone manually checking source tables, transformation logs, and governance rules before they can reply.
The agent triages incoming data tickets, checks lineage and governance rules, and drafts the root-cause explanation. A data engineer reviews and sends the final answer on anything ambiguous.
We take one real workflow, measure how it works today, and test an agent alongside your team. At the end, evidence—not momentum—decides whether it moves to production.
Day 1
Agreed baseline
Volume, hours, errors, and systems involved. Without a baseline there is no value to prove.
Days 2 to 8
Build in shadow mode
The agent works on a real sample in a controlled environment, without executing final actions. Your team verifies every relevant result.
Days 9 and 10
Before, after, and decision
If the metric improves, we propose architecture, scope, and price for production. If value does not show up, we close the proof and recommend against scaling.
What you leave with
Agreed baseline
Starting metric, scope, and current performance.
Before-and-after comparison
The same metric, measured again after the controlled test.
Go/no-go recommendation
A written recommendation to proceed or stop.
Describe the repetitive task that eats the most hours, causes the most errors, or slows your team down the most. We reply within one business day.
Every agent operates inside rules, permissions, and human gates defined before production.
01 · Deterministic code
The agent interprets, explains, and flags. Sensitive totals and business rules run in deterministic code.
Visible example Totals and tolerances are computed in code; the agent only explains the exceptions.
02 · Human approval
The agent prepares the action and explains why. A person approves exceptions, money, and sensitive data before execution.
Visible example An out-of-tolerance invoice is prepared with evidence; Finance approves or rejects it before anything runs.
03 · Complete trace
Each recommendation records its source data, rule version, agent step, and reviewer. Nothing is a black box.
Visible example A reviewer can move from the final recommendation back to the exact record and rule that produced it.
04 · Explicit boundaries
Access, amounts, systems, and allowed actions are defined outside the model and enforced by the system.
Visible example The agent cannot enter another workflow, exceed an amount, or bypass a restricted action.
05 · Continuous verification
A model, rule, or integration change reruns the agreed validation set before release.
Visible example The same before-and-after checks run again before the changed system returns to production.
We do not promise a model will never be wrong. We build the system to detect, contain, and correct the error before it becomes a cost.
Bring your IT or security lead to the second call.
Ophra and CoreWhapp are our own products. They are not sold here: each one has its own site. They are on this page as evidence that the architecture holds up outside a demo.
An internal AI agent team that works from a company's own documents and policies, with human review before each result closes.
See ophra.ai
A shared inbox where AI agents answer WhatsApp, Instagram, and Messenger using a business's real stock, pricing, and order data, plus its own CRM on Meta's official WhatsApp Business API.
See corewhapp.aiOur custom engineering work doesn't have a closed, end-to-end case study to show here yet. When it does, it will be in this section. Until then, the discipline you see in Ophra and CoreWhapp is the same one we apply to every new project.
Founder-led, from the first decision to production.
Meet the people who set the standard and answer for every delivery.
Latest edition · Volume 02
From reactive BI to proactive action with AI agents
RavencoreX MAG is where we publish what we learn taking AI into real operations: architecture decisions, governance criteria, cost analysis, and use cases with their limits.
It is not a news feed. Every piece states clearly what is evidence, what is editorial interpretation, what is hypothesis, and what is RavencoreX opinion.
We assess whether it fits a Proof of Value. If it does not, we say so.
