PRAHARI unifies RF, radar and optical contacts for a counter-drone operations team. We designed the decision surface that lets a duty officer understand a contact, choose a response and leave an audit trail without stitching three systems together over radio.
On 27 June 2021, two improvised drones carrying RDX detonated at the Indian Air Force station in Jammu. Nobody died. But it was the first confirmed drone strike on an Indian military installation — and it sent a directive through every formation HQ in the country.
The Corps of Signals already owned the EW and sensor coordination mandate. Their problem wasn't that sensors didn't exist. The problem was that they lived on three different screens, operated by three different people, correlated verbally, logged manually, and acted on through a chain of radio calls.
Against a drone doing 60 kmph, that process took up to four minutes. Four minutes is 4 kilometres.
Two site visits to a Corps of Signals unit. No cameras, no retention of network topology data, no documentation of sensor specifications. Notebooks only. We went as observers.
Six operator interviews and three shift handovers observed. Every insight extracted from conversation, not screen recordings. The informal fixes they'd already invented told us exactly what the software was missing.
Discovery to handoff in 14 weeks. 160+ annotated screens, 43 components, interaction specs for every dynamic state, and a data model brief co-written with the dev agency's tech lead.
You can't design a system like this from a studio in Bengaluru. We arranged two two-day site visits to a Corps of Signals unit at a formation HQ.
Reconstructed research context · The revealing behaviour was not contact detection; it was the paper-and-verbal ritual required to rebuild operational context at each shift.
The shift handover was the most revealing ritual. Incoming operators needed 18–35 minutes of verbal briefing because there was no structured display of what was active, pending, or cleared.
We watched a duty officer handle two training-scenario drone contacts. His eyes moved between three screens. One hand on a radio. The other writing a register entry.
The mental model was spatial — he thought in bearings and sectors — but his primary tool was a tabular RF signal log. There was a persistent mismatch between how the problem existed in his mind and how the tools presented it.
We taped that to the wall for the rest of the project.
We could not photograph the site. These three illustrations reconstruct what the visits looked like — the night watch, the handover that ends it, and the forward post that both of them depend on.
Drawn, not photographed · Illustrated reconstructions from our field notes. No identifiable person, unit or location is depicted, and details are composited and altered under the programme's NDA.
Fragmented picture. Sensor feeds were siloed. RF, radar, and optical data required mental correlation by the operator — a high-load task under pressure.
Spatial-tabular mismatch. Operators reasoned about threats in space (map, bearing, sector). Their primary interface was a table. Every decision required a translation step.
No audit trail. Engagement decisions — classify, jam, escalate — were made verbally. After-action reviews required stitching together registers, logs, and operator memory.
4-minute response gap. Against a drone doing 60 kmph, the multi-screen, multi-person, verbal-correlation process took up to 4 minutes. That's 4 kilometres of uncontested airspace.
That was the design contract.
Before we opened Figma, we ran an internal framing session around one question: What is the cost of a wrong decision — and which direction does it cut?
In C-UAS, a false negative (missed threat) is catastrophic. A false positive — triggering a jammer on a civilian aircraft — causes its own incident. The interface had to make asymmetric decision risk visible and actionable.
| Principle | What it meant in practice |
|---|---|
| Spatial primacy | The map leads. All tabular data is subordinate and accessed in context of a selected contact. |
| Triage over information | The interface shows three states: Act Now · Tracking · Cleared. Visual hierarchy enforces it relentlessly. |
| Every action leaves a trace | No verbal-only decisions. Classify, jam, escalate — all auto-logged with timestamp, operator ID, and current threat state. |
The Threat Operations Panel is the duty officer's primary interface. Live low-altitude airspace map. Contact classification overlays. Triage queue. Contact detail panel opens inline on selection. Design target: detection to engagement decision in ≤3 interactions.
Click a contact → review sensor data and classification → confirm electronic jam (called a "soft-kill" in military terms). The jammer activates, the map updates, the log entry writes itself. The duty officer never leaves the screen.
Operator clicks contact in triage queue (or Tab to select, Enter). Contact Detail Panel opens with sensor data, bearing, distance, speed visible.
Operator presses SOFT-KILL (or S key). Confirmation modal opens with jammer assignment + auto-log preview.
Operator presses CONFIRM (or Enter). Jammer activates. Map shows amber zone. Status bar updates. Log entry created automatically.
The complete engagement pipeline — from first sensor contact to electronic neutralisation — designed for ≤ 5 seconds and ≤ 3 interactions.
Alarm fatigue was a documented problem in the existing setup — too many red indicators had trained operators to discount the colour. We committed to a discipline: red means act. Period. Nothing decorative, nothing ambient. If it's red, the duty officer must make a decision.
Red. Pulsing marker on map. Top of triage queue. The operator must classify and respond.
Amber. Static marker. Middle of triage queue. Monitoring — no immediate action required.
Grey. Faded marker. Bottom of queue, collapsed by default. No action needed.
The Shift Handover Dashboard was designed to replace the 18–35 minute verbal handover entirely. A structured status snapshot readable in under 2 minutes. The design test: can a new operator walk in, read this screen, and know the full picture without speaking to the outgoing operator?
The Engagement Log was specifically designed for post-mission debriefs (the military's After-Action Review, or AAR). Every engagement decision — classify, jam, escalate, clear — auto-logged with timestamp, operator ID, and current threat state. Filterable, exportable to PDF, and dense but legible.
The sensor management screen gives the sensor operator full visibility into each unit's health, coverage, and confidence rating. When OPT-3 drops to 38% confidence due to fog, the operator sees it immediately.
A visual projection of a contact's flight path. Technically feasible, but our field research showed it created false confidence. Operators anchored to the projection and stopped updating their mental model. Cut.
Allowing adjacent units to see each other's contact picture. Politically complex, technically dependent on infrastructure we couldn't guarantee. Documented as Phase 2, not killed.
We handed off a Figma file with 160+ annotated screens, a component library with 43 components, interaction specs for every dynamic state, and a data model brief co-written with the dev agency's tech lead.
The keyboard-first interaction model required specific engineering coordination. We documented every keyboard shortcut, focus state, and tab order in a separate interaction spec because the duty officer scenario — high stress, hands busy, eyes on the map — made mouse-first interactions a design liability.
| Deliverable | Count | Description |
|---|---|---|
| Annotated Screens | 160+ | Every state, edge case, and error condition documented |
| Component Library | 43 components | Contact markers, action buttons, badges, modals, sensor cards |
| Interaction Specs | Complete | Keyboard shortcuts, focus states, tab order, animation timing |
| Data Model Brief | Co-written | Contact, Event, Sensor Unit, Operator Session schemas |
Red is exclusive to contacts requiring action. Amber means watch; nominal states recede.
The spatial picture leads, triage orders attention, and detail explains the evidence behind it.
A fused answer never hides its inputs. Operators can see which sensors agree and how recently.
Shortcuts, focus order and confirmation states support eyes-on-map operation without mouse hunting.
That handover metric was the one we were most proud of — because it came from a user need we'd observed ourselves, not from a brief.
We never got iterative feedback access to users after the field visits. Every decision after week three was made on conviction, tested against our principles, and defended in internal critique — not validated with real users.
If we did it again, we'd push for at least one remote usability session with operators using a high-fidelity prototype before the dev handoff. The handover dashboard would have been redesigned again.
When you can't ask the user, you have to know the problem well enough to argue with yourself. The field visits weren't research luxury — they were the entire foundation.
When you can't ask the user, you have to know the problem well enough to argue with yourself.