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IDEAS SOLUTIONS REAL-WORLD IMPACT DIFFERENT INDUSTRIES. A STRONGER TOMORROW 
We built an end-to-end medical imaging platform that helps radiologists detect critical findings faster, improving diagnostic accuracy and patient outcomes.
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Every project is different, but the goal is the same solve meaningful problems and create lasting value.
“Tathastha brought clarity, speed and deep technical expertise to a complex problem. The impact has been transformational.”
Clinicians talk to their patients like normal. The note writes itself, in the system they already use.
Doctors spend more time typing than treating. Every extra minute charting is a minute not spent with a patient — and most clinics can't or won't change the record system they've used for years.
The suite centers on a real-time visit copilot that listens during a consult and drafts a structured note as the conversation happens, plus a browser extension that injects that copilot directly into the clinician's existing EHR tab rather than asking them to open a separate app. Behind it, an EHR-connect layer normalizes OAuth and data mapping across a dozen different EHR platforms, and a serverless, event-driven pipeline handles asynchronous note generation whenever a recording is uploaded outside the live-visit flow. Patient-facing intake and messaging apps extend the same backend to the pre- and post-visit parts of the workflow.
The direct effect is fewer minutes per visit spent typing and more spent with the patient — the note is drafted automatically, in the clinician's own EHR, in the note format their specialty already uses.
Because the integration layer is vendor-agnostic, a clinic doesn't need to change record systems, and the product isn't locked to one AI vendor if pricing, quality, or availability shifts. Time-saved and adoption figures are intentionally not stated here — they should only be published from real, independently verified usage data.
Describe what you want. Answer a few questions. Walk away with a real plan — goals, tasks, and a way to track them weekly.
Most people know the outcome they want — get fit, launch a project, hit a revenue number — but stall at turning it into concrete steps. Teams have the same problem at a bigger scale: strategy exists on a slide, but nobody's clear on this week's tasks.
A conversational agent asks a small, fixed set of clarifying questions about the user's goal, proposes 3–5 high-level objectives for confirmation, then expands confirmed objectives into a full tree of goals, sub-goals, and tasks — each scored on effort, impact, and control. That plan is tracked weekly with progress and risk indicators, streaks, and a daily planning view, inside a multi-tenant product that also handles teams, coaching roles, and subscription billing.
What used to take a working session with a coach or a blank page and good intentions now takes one guided conversation. The output isn't generic advice — it's a structured, trackable plan with tasks the user (or team) can act on the same day.
For teams, the same engine scales from one person's personal goal to an organization's strategic goals, with the accountability layer — weekly tracking, coaching, risk flags — built in rather than bolted on.
Every morning, a plain-English "here's where things stand" lands for every person, goal, and team — with no one having to write a report or run a query.
With activity spread across every goal, task, and team, leaders either read everything themselves or read nothing. Neither scales, and by the time a weekly report gets written, it's already stale.
A scheduled orchestration job runs every 30 minutes, fans out across every user, goal, organization, and team, and asks an LLM to summarize recent activity and flag risks — with batching, rate limiting, retries, and token accounting so it doesn't fall over at scale. A second version adds a delivery step, formatting the day's summaries into a report. Separately, a Model Context Protocol (MCP) server exposes the underlying database as a safe, read-only query tool — restricted to an explicit table allowlist and SELECT-only access — paired with an agent client that adds lightweight retrieval over past summaries so it can answer follow-up questions with memory of prior context.
Instead of a leader hunting through dashboards or waiting for a weekly report, a concise, plain-English summary is simply waiting for them each day — for themselves, for a team, or for the organization as a whole.
The natural-language data agent points toward a future where anyone can ask a question about progress directly, instead of every stakeholder needing their own custom report built for them. The read-only data-agent layer is currently a proof of concept, not yet a hardened service.
Ask what a lab result means and get an answer grounded in real medical literature — not a generic guess, and not a search you have to interpret yourself.
A biomarker result like HbA1c or TSH comes back with a number and a reference range, and most people are left to search the internet themselves — with no way to know if what they're reading is accurate or relevant to them.
A supervisor agent first classifies each incoming question — small talk, a question about the user's own profile, or a scientific question about a biomarker — and routes accordingly. Profile questions go to a small, isolated agent that answers strictly from the user's own stored data. Scientific questions go to a retrieval agent that searches a structure-aware index of biomedical research papers, drafts an answer, scores that draft against a fitness function, and — if the score falls short — refines and re-synthesizes before responding, rather than returning its first-pass answer.
Instead of an AI assistant confidently making something up about a health topic, this one is built to know the difference between "I know this about you" and "here's what the research says" — and to double-check its own scientific answers before giving them. This is a pre-production system: it has not been clinically validated, and no accuracy or outcome claims should be attached to it without that validation first.
Take a photo. Get back a measured size, an estimated depth, and a breakdown of tissue condition — the kind of read a clinician would normally need to be in the room to give.
Monitoring a healing wound — a pressure sore, a diabetic ulcer — usually means an in-person visit just to measure it. For homebound or remote patients, that's a real access barrier.
An uploaded photo is run through a custom-trained segmentation model to isolate the wound region from surrounding skin. Principal-axis analysis on that mask produces length, width, and area, calibrated against a reference object visible in frame. The cropped wound region is separately passed through a monocular depth-estimation model to produce a relative depth estimate, and a color-space analysis buckets the visible tissue into categories associated with different stages of healing.
The core idea — structured wound measurements from a phone photo, without requiring an in-person visit — is demonstrated and working end-to-end. Important caveat: depth is a relative, not an absolute, measurement, and size calibration currently assumes a fixed reference object rather than detecting one in the photo. This should be treated as a promising prototype, not a clinically validated measurement tool, until those two points are addressed and independently verified.
Two quick demonstrations, built to prove the idea works before investing in a full product: one turns existing session notes into a formatted clinical note, the other turns a spoken conversation into one.
Before committing real engineering time, it's worth proving an idea works on a small scale — these two demos exist to answer "can this actually work" quickly and cheaply.
The first demo takes an uploaded document, extracts its text, and prompts an LLM with a note-format-specific template — SOAP, DAP, BIRP, and several others — to produce a formatted clinical note. The second takes an uploaded audio recording, transcribes it with an automatic speech recognition model, optionally labels speaker turns, and then runs the same kind of prompt-templated note generation over the resulting transcript.
Both ideas were validated quickly and cheaply, which is exactly the point of a demo: prove the concept works before spending real engineering budget on a hardened pipeline. Both fed directly into the decision to invest in the fuller async note-generation pipeline described in our clinical documentation suite above.
Tell us what you're trying to achieve. We'll help you explore the right approach from idea to impact.
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