SIH26122: Intelligent Data Capture & Schedule-Linking Layer for Infrastructure Project Man — SIH 2026 | Wynit

SIH26122: Intelligent Data Capture & Schedule-Linking Layer for Infrastructure Project Man — SIH 2026 | Wynit

What they'll ask you

13

Generated from the brief's own wording and the competition's published rules, never from a guess about what this ministry prefers.

Exact schema of the Primavera or MS Project exports?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Specific taxonomy of discipline-wise terms used by Oil India?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Target latency for the fuzzy matching and schedule update?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.

10 morethe ones a panel gets to if there is time

Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.
The brief asks for multi-format ingestion. How would you build that?
Decoded from SIH26122 itself: Ingest heterogeneous inputs like free-text reports, spreadsheets, and schedule exports. The brief asks for it by name.
The brief asks for conversational interface. How would you build that?
Decoded from SIH26122 itself: Provide a voice or chat interface for site supervisors to log activity start and end times. The brief asks for it by name.
The brief asks for fuzzy matching engine. How would you build that?
Decoded from SIH26122 itself: Link extracted discipline descriptions to the correct plan node and flag unmatched activities. The brief asks for it by name.
The brief asks for schedule auto-update. How would you build that?
Decoded from SIH26122 itself: Update actual start and end dates in the PMIS with a confidence score and audit trail. The brief asks for it by name.
The brief asks for execution repository. How would you build that?
Decoded from SIH26122 itself: Produce a structured dataset for live analytics and historical project memory. The brief asks for it by name.
Where does your data come from: a published source, one you collect, or one you generate?
No dataset is attached to this problem statement, so sourcing it is part of the work and nobody told you that.
Why not use what already exists? Name the closest thing to this that is already running.
A team that has not named the alternative themselves is answering this for the first time in the room.
Which single thing will you demonstrate end to end, start to finish, with nothing skipped?
Ours, not a rule: a narrow thing that fully works survives questioning better than a broad thing that half works. If nobody on the team can name it, that is the finding.
Show me this working: the jury will check whether your prototype can successfully ingest at least two or three varied input formats, correctly perform fuzzy matching to link them to schedule nodes, and generate a clear audit trail for the updates.
This is the evaluator read for your problem statement, decoded from the brief's own wording.

The government's original description (491 words)

• Background Infrastructure project schedules cascade from macro milestones (L1) down to micro, executable activities (L5/L6),spanning multiple engineering disciplines - civil, piping, static/rotating equipment, electrical, instrumentation, HSE- each executing and reporting in parallel. While the baseline plan is well-structured (Primavera/MS Project), actual execution data flows back through daily progress reports, site diaries, discipline-wise spreadsheets, and verbal supervisor updates, each in its own format and cadence, largely disconnected from the L5/L6 activity IDs in the plan. • Problem Description There is no reliable, low-friction mechanism to capture actual start/end times of L5/L6 activities across disciplines and auto-link them back to the plan. Input quality varies with manpower skill, reporting discipline, and format.Field execution is often more granular than the planned WBS, and different disciplines describe the same physical progress differently (e.g., 'spool erected' vs. the plan's 'Erect Line 24?-XX').Consequently: ? Actual progress data is fragmented, delayed, and inconsistently structured across disciplines and contractors. ? Manual reconciliation with the baseline schedule is slow, error-prone, and often lags the schedule update cycle by days or weeks. ? Downstream performance analytics, delay/ risk analysis, and forecasting inherit this poor-quality, late data- undermining the AI performance-monitoring stack that depends on it. ? Once a project closes, the hard-won knowledge of what actually happened - real durations, real bottlenecks,real deviations from plan - is rarely captured in a structured, queryable form, so it is lost rather than feeding future project planning. • Expected Outcome/Solution ? Ingest heterogeneous discipline-wise inputs - free-text daily reports, spreadsheets, scanned diaries,Primavera/MS Project exports - and extract activity-level actual start/end events. ? Offer an LLM-based conversational or voice interface ('time agent') for site supervisors across disciplines to log activity start/end with minimal friction, replacing rigid manual forms while still producing structured output. ? Fuzzy-match and link extracted discipline-specific activity descriptions to the correct L5/L6 plan node,handling terminology differences and granularity mismatches, and flag unmatched/new activities for planner review rather than silently dropping them. ? Auto-update actual start/end dates in the schedule/PMIS in near real time, with a confidence score and audit trail per entry. ? Produce a clean, structured, discipline-tagged actual-progress dataset that serves two purposes: (a) live input for performance analytics, delay/risk pattern discovery, and forecasting, and (b) a foundation for institutional memory building - a growing, queryable repository of real project execution patterns (actual durations, recurring delay causes, discipline-wise productivity) that future projects can learn from, instead of that knowledge staying locked in individual supervisors' experience or scattered paper records. A working prototype demonstrating ingestion of 2â€“3 varied input formats (e.g., a free-text daily report and a discipline spreadsheet), extraction, and schedule-linking logic would be ideal; full production-grade OCR/ASR is not required. • Relevant Data Availability Anonymized/ sample daily progress report formats, sample L5/L6 schedule extracts, and illustrative discipline-wise (civil/ piping/ electrical) site-diary or spreadsheet templates can be shared under NDA with Institute/ Authorised person. Live project data will not be shared; teams should work with synthetic/sample data of similar structure.