RIV: AI Task Planning Assistant
- 60% faster task decomposition versus manual ticket writing
- ~8 hours/week freed up per project manager
- 90%+ accuracy on named entity extraction (dates, names, tags)
- < 3 seconds median pipeline response time
RIV is an AI task planner for product managers and ops leads who lose hours each week converting vague briefs into proper tickets. It decomposes unstructured requests into structured, ready-to-assign subtasks and surfaces hidden project risks before sprint planning.
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Project Details
The startup behind RIV had a clear product vision and a growing PM team that was drowning in manual ticket work. They knew exactly what was broken: vague briefs turned into hours of rewriting, risks surfaced too late, and context got lost across Slack, Notion, and Jira.
Client- Product company
Industry- Project Management / SaaS
Solution- GPT-Powered AI Task Planning assistant
Service- GPT-Powered AI Task Planning assistant
Platform- Web (admin + end-user UI) + REST API
Scope- AI/ML, Backend, Frontend, QA, DevOps
Duration- ~5 months
Location- US
Business Challenge
The client came to LITSLINK after their internal Notion + Jira workflow stalled at scale. PMs were spending 30 to 45 minutes per request rewriting briefs into proper tickets, and the ops team had no reliable way to flag downstream risks before sprint planning. They needed an AI assistant for generating subtasks and an AI risk prediction tool that could plug into their existing PostgreSQL stack without forcing a rewrite of the rest of the platform.
The client wanted one tool that could read a free-text request and return either a list of proposed subtasks or a list of likely risks, with names, dates, and tags pre-extracted, and a single-click approve-and-assign flow. Three issues kept showing up in client interviews:
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Lost context in handoffs
Names, dates, and tag references mentioned in chat or briefs rarely made it into ticket systems unedited. The same details got re-typed 3–4 times across Slack, Notion, Jira, and Google Calendar.
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Vague briefs that don't translate into work
A line like "Complete all pages in mobile app design" needs at minimum 3 to 6 concrete subtasks, owners, due dates, and tags. About 22% of briefs required two or more rewrites before any actual work could begin with PMs spending up to 45 min per request on that process.
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Risk blind spots
Without automated risk assessment for tasks, dependency and capacity issues kept surfacing only after deadlines had already been pushed.
Technologies Behind the AI Anomaly Detection Assistant
Our AI Task Management Solution
We built RIV as a custom AI task management solution wrapped around three GPT-driven workflows: subtask generation (task breakdown automation), risk prediction (an AI tool for predicting task risks), and named entity extraction. Each workflow runs through the same pipeline: input validation, prompt construction, GPT API call, post-processing, and DB write.
The user picks a mode (Risks or Tasks), types a request, and gets back a structured list with proposed dates, tags, and assignees. Each row has its own “Approve & Assign” button, so PMs can accept good suggestions and discard the rest without going back to a blank ticket.
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AI-Powered Task Decomposition
The assistant interprets open-ended, natural-language briefs and translates them into structured subtask lists with proposed dates, tags, and owners. It’s closer to talking to a knowledgeable AI planning assistant than filling in a form.
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Risk Prediction
The AI risk prediction tool runs the same pipeline with a different prompt, surfacing likely blockers, dependency conflicts, and capacity issues before sprint planning.
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Named Entity Extraction
Beyond answering the main request, the system pulls dates, people, project references, and tags directly from the brief text, pre-filling fields that PMs used to type manually.
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Approve & Assign Workflow
Each proposed subtask or risk has its own one-click accept button. PMs can approve, edit, or discard row by row — no all-or-nothing output.
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Persistent Storage & Audit
Every approved result is saved to PostgreSQL with a full audit log. Subtasks can be edited or revisited later, and the data feeds downstream integrations with Jira, Asana, or ClickUp.
Ready to build an AI assistant for generating subtasks and predicting risks?
Request a Similar SolutionScrum Methodology
Project Journey
Discovery and scoping kicked off the RIV process — defining the two core modes (subtask generation vs. risk prediction), mapping the entities the pipeline needed to extract (names, dates, tags, project references), and aligning on integration architecture with the client’s Jira and PostgreSQL stack. From there, Scrum sprints moved the work forward, with early demos surfacing edge cases missed during requirements gathering.
- Weeks sprint cycles
- 2
- Sprints completed
- 8
- On-time delivery
- 97%
- Team members
- 5
How the AI Task Planner Works
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User submits request
PM types a brief in natural language via the web interface.
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AI detects intent
NER and classification models extract entities — names, dates, tags, and project references.
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Prompt construction
The system assembles a structured prompt with few-shot examples and the user input.
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GPT generates output
The model returns a structured list of subtasks or predicted risks within seconds.
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Review & approve
PM reviews proposed items, edits inline, and approves row by row.
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Save & sync
Approved items are written to PostgreSQL and can sync to Jira, Asana, or ClickUp.
Scrum Process Flow
Two-week sprints. Early demos surfaced edge cases fast — ambiguous dates, shorthand entity names, requests that flipped between Tasks and Risks mid-sentence. Discovery resolved all three before the build began: two core modes defined, entity taxonomy mapped, Jira and PostgreSQL integration agreed.
Inside Each Sprint
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Daily Scrum
15-min sync every morning
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Retrospective
Inspect & adapt process
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Sprint Review
Demo to stakeholders
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Increment
Shippable product update
-Timeline
Five phases, clearly defined
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Discovery & Product Workshop
1–2 weeks
- Aligning on use cases and project goals
- Defining task-domain entity taxonomy
- Mapping integration touchpoints and architecture
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Prompt Prototyping
2 weeks
- Prompt variants tested against 300+ real briefs
- Dual-mode design (Tasks / Risks) validated before full build
- Response card structures and approve flows defined
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Agile Development (Sprints)
~4 months
- Engineering is focused on 2-week sprint cycles
- NER + GPT layer tested against real-world PM inputs
- Edge cases surfaced early via sprint demos
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QA & Testing
2 weeks
- Model adjustments based on observed PM behavior
- Testing against varied natural-language project briefs
- Integration and performance validation (PostgreSQL, API)
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Launch & Support
Ongoing
- Post-launch monitoring for production edge cases
- Ongoing prompt refinements based on real user data
- Support handoff with full documentation and updates
UI/UX Design
Most teams treat the prompt as a configuration file. We treat it as a UX surface. The user types a sentence. The model has roughly 3 seconds to return something useful enough that a busy PM does not tab away. That window forced three design rules:
- No stalling
If GPT takes longer than 3 seconds, we show a partial result with a “still thinking” indicator instead of a generic spinner. - No empty states
If parsing fails, we show the raw GPT text in an editable field so the user can salvage it instead of retyping the whole brief. - No forced corrections
Every suggested field, date, tag, and assignee is editable inline.
We ran 4 rounds of prompt experiments with the client’s PMs before locking the production prompts. Each round used a fixed test set of 50 briefs, and we tracked accuracy, edit rate, and time-to-approve for every variant. By round 4, the median time from request to approved subtask list was under 90 seconds.
Results
Before
- PMs spent 30–45 minutes per request rewriting briefs into proper tickets — all manual.
- ~22% of briefs needed two or more rewrites before the team could start work.
- Names, dates, and tags were re-typed 3–4 times across Slack, Notion, Jira, and Calendar.
- Planning and ticket creation were fragmented across multiple tools. No unified interface.
After
- 65%+ faster decomposition: 8–12 minutes per brief, including review and edits.
- ~3% of generated subtask sets needed a full rewrite — the rest were edited inline.
- Named entity extraction filled 90%+ of date and tag fields automatically.
- Subtask generation and risk analysis unified in a single web interface — one tool, one flow.
The Impact
By the end of the engagement, the client estimated each PM was getting back roughly 8 hours per week that used to go into manual decomposition and ticket cleanup. Across a team of 14 PMs, that is the equivalent of about 3 full-time roles freed up for higher-leverage work.
This project demonstrates what’s possible when AI risk assessment software is built around real PM language. The NER layer, the GPT pipeline, and the approve-and-assign flow aren’t interesting technologies in isolation. They’re interesting because together they close the gap between what a PM writes and what a system can do with it.
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