gridmap's AI proposes a mapping and a confidence score for every unmapped source value. Confident answers are applied, the rest wait for your team, and every change is logged.
Translating codes between systems is repetitive. The same patterns come up again and again, which is the kind of work automation handles best.
With many values to map, small inconsistencies creep in. The AI sees your existing mappings as examples and can be limited to a value set, which keeps its answers consistent.
Source systems add codes regularly. Keeping up means someone has to notice, look them up and map them, or automate it.
How many values are unmapped? Which gridmaps need attention? gridmap shows the unmapped count for every gridmap on the dashboard.
Anthropic, OpenAI or Google AI. Switch at any time without losing your mapping history.
Confident mappings are applied; lower scores are flagged for review.
Tell the AI about your domain, naming conventions and edge cases. It also sees examples from mappings your team has already made.
Scheduled syncs, with AI mapping of the new values straight after each one. Chain jobs so one starts when another finishes.
Build keys from several source columns, and map each value to several output columns.
Single and batch lookups, full and incremental loads, with per-key access control.
Every suggestion comes with a score, so reviewers know what to trust and what to check.
Approve in bulk or one at a time. AI only fills unmapped values and never overwrites an existing mapping.
Every AI mapping is logged like any other change, so you can see what was applied and when.
Not every value needs a human decision. Pull values straight from your sources and reuse them across gridmaps, so the same list is maintained once. Where a translation rule is clearly defined, AI applies it for you, and your team only judges the ambiguous cases.
Pull values straight from your sources and reuse them across gridmaps. Data that already lines up needs no mapping step.
When the logic is well defined, AI applies the mapping with a confidence score, so obvious translations don’t wait on a person.
Your team spends its time on the ambiguous values, not the repetitive ones a rule already covers.
AI mapping reads your source values (codes, labels, identifiers) and proposes a target value for each, based on what the value means rather than how it is spelled. It handles abbreviations, domain terms and common code patterns. Instead of someone looking up every code by hand, the AI proposes the mapping and your team reviews it.
Accuracy depends on the quality of the source data and on how well the target values are defined. Attaching a value set limits the AI to answers you allow, and your instructions and existing mappings give it context. Every suggestion carries a confidence score, so you can let confident answers apply and send the rest to review.
Yes. You can give the AI instructions about your domain, naming conventions and edge cases, for example: "EMEA means Europe, Middle East, and Africa. SWE maps to Sweden." On a gridmap with several output columns, each column can have its own instructions.
Scheduled delta syncs pick up new source values from your connected databases. With auto-map switched on, the AI maps them right after the sync: confident answers are applied and the rest wait for review. Notifications can tell your team by email, Slack or Teams when new unmapped values arrive. Nothing is silently dropped: a new value stays in the gridmap, visibly unmapped, until someone handles it.
You decide. Suggestions at or above your confidence threshold are applied automatically and the rest wait for review, so a higher threshold sends more of them to a person. Every AI change is logged, so you can check afterwards what was applied. The AI saves time, and your team stays in the loop.
gridmap supports Anthropic (Claude), OpenAI (GPT) and Google AI (Gemini). You can switch providers at any time without losing your mapping history, and you can use your own API key. AI mapping is included from the Basic plan.
Each gridmap, and each output column if you want, has a minimum confidence score (for example 0.85). The AI returns a confidence score with every suggestion. If the score meets the threshold, the mapping is applied. If not, the value waits for review, or, if you prefer, gets a placeholder such as UNKNOWN without review. This lets you balance speed against accuracy.
gridmap is live. Ask for a free license that never expires, or talk to us about a plan with AI mapping.