AI
AI-ready data, and agents you can govern.
For most firms the data, not the model, is what holds AI back. Masseef's data already says what it is, when it was known and where it came from, so an agent can use it without a guide. Agents connect over MCP and do real work, with narrow permissions, every action recorded, and a person approving any change to a value.
AI-ready data
The context an agent needs is already in the data.
An agent gives a right answer only when it knows what a field means, which date a number belongs to and where it came from. Most firms are told to add that as a semantic layer or a knowledge graph over their stores. In Masseef the data model already carries it. An agent works here with far fewer tokens and retries than on generic key-value data.
Meaning
Every type and field has a name and a description, kept with its history. An agent reads the same catalog your team does.
Real links
A position links to its security, and the security to its issuer and to whatever it holds. An agent follows the graph instead of matching names.
Time
Every value carries when it applied and when it was known, so a point-in-time question gets a point-in-time answer.
Provenance
A mastered value records its sources and the rule that chose it, and can be replayed to show it matches exactly.
Agents at work
Your own data over MCP, and the work that goes with it.
Data vendors now publish their data over MCP. Masseef does it for yours: your golden copy, positions and config, as of any date. The same connection lets an agent do the daily work of running that data.
Works the exception queue
Reads each exception with its evidence, closes the noise with a note on the record, and proposes the fix for the rest.
Changes the configuration
Drafts the change and dry-runs it against current data to show which values would move, then submits it for approval.
Answers the auditor
Lists what was restated between two dates, shows the rule in force when a value was produced, and replays it to prove the result.
Runs the reports
Picks a report from a vetted catalog and fills in its parameters. It never writes the query.
Human in the loop
A person approves every change to a value.
Routine queue work goes straight through. Anything that would change a value becomes a change request.
“Accept the 14 Jun EUR 6M curve with 13 tenors, the missing one is a known holiday.”
Pending. Evidence and the proposed change attached.
A person reviews and approves, or sends it back.
Who proposed, who approved, when, and why.
Narrow permissions
Each tool needs its own grant, and an agent holds only the grants you give it. Its SQL access is read-only.
Every action recorded
What an agent proposes, notes or closes is recorded under its own identity, next to the person who approved it.
No special path
An agent goes through the same entitlements and the same approval queue as a person on your team.
Automated research
Automated research is only as good as its point-in-time data.
For decades a quant built each backtest dataset by hand and knew its quirks: which vendor restated, how far to lag fundamentals, which years carry survivorship bias. That knowledge lived in the researcher's head, and it worked because a person looked at every test.
An agent runs thousands of hypotheses and nobody looks at each one. One look-ahead leak doesn't spoil one backtest. It produces winning-looking strategies across all of them, and it survives the usual multiple-testing checks because the pattern really is in the data, just not in reality.
Masseef keeps when each value was known, so a backtest asks for what was knowable on the day and gets exactly that.
Point your agent at it.
We'll connect an AI agent to a running Masseef and let it answer your questions, as of any date, in front of you.