Work Allocation
Hours by advisory category for the current month.
Current Focus
Live work status and availability.
Context Engineering Rollout
Current task: Engineering Workflow Prompt Pack v1
Drafted reusable engineering context packet structure. Mapping recurring software tasks into code synthesis, debugging, and customer request decomposition workflows.
Context Engineering Rollout
ActiveReusable context templates, retrieval patterns, and agentic search workflows for engineering teams.
Engineering Workflow Acceleration
ActiveMapping bottlenecks across code review, task clarification, rework, and AI-assisted development loops.
Local AI Infrastructure
Awaiting InputPlanning local/private model workflow for Sensitive IP handling, pending licensor and data-handling constraints.
Recent Work Blocks
Granular work entries with progress summaries and scope tags.
| Jira Key | Issue Type | Summary / Definition of Done | Epic | Hours | Status | Scope |
|---|---|---|---|---|---|---|
| EVO-AI-101 | Task | Engineering Bottleneck MappingDefinition: identify recurring friction points across context loss, test handoff, debugging loops, and customer-driven rework. Output: bottleneck map plus candidate automation list. | AI Transformation Roadmap | 2.25 | Done | Advisory |
| EVO-AI-114 | Story | Context Brain SetupAs an engineering team, we need reusable context packets so recurring software tasks can be accelerated through structured AI-assisted workflows. | Context Engineering Rollout | 3.00 | In Progress | Advisory |
| EVO-AI-128 | Spike | Customer-Specific Software Delivery Scope ReviewResearch spike to determine whether requested customer delivery support fits advisory capacity or requires a separate implementation SOW. | Customer-Facing Support | 1.50 | Requires SOW | Beyond Advisory |
| EVO-AI-073 | Task | Series A Technical Narrative ReviewPrepare AI transformation narrative, technical defensibility points, and diligence support notes for investor-facing conversations. | Series A AI Narrative | 2.00 | Done | Advisory |
Availability
Upcoming availability windows and work mode.
Bi-Weekly Summary Preview
Generated from logged work blocks and linked evidence.
- ✓Completed initial bottleneck map for AI-assisted engineering workflows.
- ✓Drafted context packet system for recurring software development tasks.
- ✓Prepared investor-facing AI transformation narrative inputs.
- ✓Identified one work item likely requiring separate implementation SOW.
Evidence Feed
Linked commits, docs, architecture notes, and meeting summaries.
3 commits linked to context engineering templates.
Private model deployment considerations for sensitive IP.
Action items from AI workflow training prep.
Time Ledger
Detailed chronological view of advisory work blocks, progress, evidence, and scope classification.
9:00–11:15 AM
Engineering Workflow Bottleneck Mapping
Reviewed current software delivery patterns and mapped recurring bottlenecks across task definition, context loss, debugging, testing handoff, and customer-driven scope changes. Output is an initial bottleneck map and candidate automation list.
2:30–5:30 PM
Context Brain Setup
Outlined the first version of a context memory system for recurring engineering work. Drafted data model for task packets, reusable context templates, decision logs, and retrieval prompts.
10:00–11:30 AM
Customer-Specific Software Delivery Scope Review
Reviewed requested customer delivery support and identified that the work may require active implementation ownership beyond advisory capacity. Recommended separate scope and engineering owner.
Jira Issue Map
Advisory work represented with Jira-style issue keys, issue types, epics, definitions, and scope boundaries.
Engineering Workflow Acceleration
Definition: Identify and reduce engineering bottlenecks through AI-assisted workflows, context reuse, code-review acceleration, and repeatable development patterns.
Context Engineering Rollout
Definition: Build reusable context packets, internal knowledge retrieval patterns, and agentic search practices for the software and engineering teams.
Local AI / Sensitive IP Infrastructure
Definition: Advise on local, on-prem, or air-gapped AI workflows for handling Sensitive IP without exposing protected technical material to unapproved AI systems.
Map Engineering Bottlenecks
Definition of Done: Deliver a concise bottleneck map identifying recurring delays, manual rework, context loss, testing friction, and AI uplift candidates.
Create Context Packet Workflow
User story: As an evoSonic engineer, I need reusable context packets so I can reduce repeated explanation and accelerate AI-assisted development.
Assess Customer Delivery Scope
Spike definition: Determine whether a customer-specific request fits within advisory capacity or requires a separate implementation sprint/SOW.
Jira Nomenclature for Advisory Scope
How advisory work will be represented in evoSonic’s Jira operating model.
| Issue Type | How it is used in this advisory engagement | Example |
|---|---|---|
| Epic | Large advisory objective or transformation workstream containing stories, tasks, spikes, bugs, or sub-tasks. | EVO-AI-010 Engineering Workflow Acceleration |
| Story | User-centered workflow improvement, usually framed around a team need or internal capability. | EVO-AI-114 Context Packet Workflow |
| Task | Discrete advisory, architecture, documentation, training, or review activity with a clear output. | EVO-AI-101 Engineering Bottleneck Mapping |
| Spike | Research or scoping activity used to decide feasibility, risk, scope, or whether a separate SOW is required. | EVO-AI-128 Customer Delivery Scope Review |
| Sub-task | Granular step within a task or story, such as preparing notes, reviewing a PR, or drafting a prompt pack section. | EVO-AI-114a Draft first context packet template |
| Bug | Workflow, integration, or automation defect discovered during advisory or implementation support. | EVO-AI-203 Agent workflow misroutes repo context |
AI Transformation Roadmap
Executive-level AI-native operating model, sequencing, and priority map.
Engineering Workflow Acceleration
Bottleneck identification, context systems, AI-assisted delivery loops.
Local AI Infrastructure
Secure, local, and air-gapped AI workflows for sensitive technical material.
Team Enablement
Software, embedded, leadership, and marketing AI adoption training.
Series A AI Narrative
Technical diligence support, AI moat narrative, transformation evidence.
Robotics Training Stack Strategy
Training/simulation layer that complements evoSonic’s deployment-side hardware.
Availability Calendar
Sample week view. Time is managed against advisory capacity and deep-work windows.
Bi-Weekly Report Generator
Automatically compiles Jira-aligned work items, work blocks, hours, outcomes, blockers, and next steps.
| Section | Content Preview | Status |
|---|---|---|
| Total Hours | 32.75 hours logged this period across 6 workstreams. | Ready |
| Top Outcomes | EVO-AI-101 bottleneck map, EVO-AI-114 context packet v1, investor narrative review, local AI planning notes. | Ready |
| Blockers | Need repo access, software team validation, licensor guidance for sensitive IP. | Active |
| Scope Flags | Customer-specific software delivery may require separate SOW. | Review |
Evidence Feed
Linked work artifacts with controlled confidentiality labels.
Reusable engineering context packet schema and first templates.
Executive summary for engineering and leadership alignment.
Local-only model workflow notes. Details omitted from dashboard summary.
Training agenda and next-step owner map.
Engagement Settings
Current advisory model settings.
| Engagement Type | Strategic AI Advisory |
| Reference Capacity | ~20 hrs/week averaged monthly or bi-monthly |
| Report Cadence | Bi-weekly |
| Scope Flagging | Enabled |
| Confidentiality Tags | Enabled |
Confidentiality Controls
Sample visibility rules for safe reporting.