Mohamed Landolsi
Technical Business Analyst
I translate business objectives into requirements, API contracts, and data workflows for AI-native SaaS products, then trace every line back to why it matters.
↓ Download CV (PDF)I make sure engineering builds the right thing, and that stakeholders can see, trace, and verify why each piece exists. I sit between product/leadership and engineering, translating "what the business needs" into specs a dev team can build from without guessing.
- Business Requirements Documents (BRD)
- Requirements Traceability Matrix (RTM)
- User stories with acceptance criteria
- BPMN process models (As-Is / To-Be)
- ERDs and data models
- API contracts
- Prioritized, weighted backlogs
- KPI frameworks
- UAT test plans
- AI-native / SaaS startups
- Product teams needing a business↔engineering bridge
- Consultancies running client BA engagements
- Technical Business Analyst
- Junior Business Analyst
- Product Analyst
- Solutions Engineer
- Led requirements elicitation for a 3-service AI-native SaaS platform, authoring 40+ requirements and an RTM that aligned a 4-person dev team with business goals.
- Conducted competitive/market analysis and modeled actor-based use cases (8 actors) to define product positioning against six identified market gaps.
- Evaluated monolith vs. microservices trade-offs, defining a Federated SOA and multi-tenant data model (PostgreSQL RLS) balancing cost with GDPR compliance.
- Built a weighted Jira prioritization matrix (Value 40%, Urgency 25%, Risk 20%, Architectural Dependency 15%), delivering MVP to 6 European clients within a 6-month Lean Scrum cycle.
- Bridged CEO and engineering, translating AI capabilities (anomaly detection, vector databases, LLM fallback chains) into user stories, acceptance criteria, and KPI frameworks.
- Integrated third-party services (LLM providers, Apify, Tavily) into the platform's AI pipeline, defining API contracts and data-exchange requirements.
View the problem: fragmented GTM tooling landscape ▶
From the PFE report's introduction: the fragmented, manual-handoff tooling landscape most B2B GTM teams navigate, contrasted with the unified platform approach.
Fragmented tooling landscape vs. unified platform approach (click to view full size).
View example artifacts: RTM excerpt & architecture diagram ▶
| ID | Requirement | Business Objective | Test Case |
|---|---|---|---|
| FR-014 | System shall route AI queries through a fallback chain if the primary LLM provider times out. | Maintain 99% uptime for client-facing AI features | TC-014 |
| FR-021 | System shall enforce row-level tenant isolation on all client data queries. | GDPR compliance across EU client base | TC-021 |
| FR-033 | System shall log anomaly-detection flags with severity tier for reviewer triage. | Reduce manual QA review time by 30% | TC-033 |
Illustrative reconstruction for portfolio purposes: client-confidential values generalized; structure and role reflect the actual engagement.
View architecture trade-off analysis ▶
| Monolith | Federated SOA (chosen) | Microservices | |
|---|---|---|---|
| Team fit | 1 team | Small team, per-service ownership | Requires dedicated per-service teams |
| Failure isolation | One crash affects all domains | Service crash stays within one domain | Partial failures complex to diagnose across network |
| Data security | Shared schema | Schema isolation per context | Independent databases |
Rationale: bounded-context separation (DDD) without microservices' distributed-transaction overhead, fitting a small team and a tight cost ceiling.
View methodology & KPI framework ▶
| Why Scrum | Lean-team adaptation |
|---|---|
| Product scope shifted with client feedback | Founder acted as Product Owner and Scrum Master; direct client meetings, Jira backlog, sprint priorities |
| Features needed calibration on real usage | 3× weekly stand-ups; source on GitHub, async coordination on Slack |
| External API constraints surfaced late | Formal backlog, sprint backlog, Definition of Done, and RTM maintained throughout |
| KPI category | Metric | Target |
|---|---|---|
| Functional | Content approval rate | High rate, strong brand alignment and prompt quality |
| Operational | Signal freshness delay | Minimize delay between signal capture and dashboard |
| Quality | Tenant isolation incident count | Zero cross-tenant access events |
| Economic | LLM cost per tenant | Keep the large majority of requests within free-tier quotas |
View real use case diagrams: Portal, Tropicc & Oceanss ▶
From the PFE report's functional analysis chapter, one use case diagram per bounded-context service.
Portal, identity and core management (click to view full size).
Tropicc, LinkedIn content generation (click to view full size).
Oceanss, go-to-market intelligence (click to view full size).
View real BPMN workflows: content generation & GTM intelligence ▶
To-be process models from the PFE report, modeling actor responsibilities, decision points, and cross-service data movement.
To-be LinkedIn content generation process (click to view full size).
To-be GTM intelligence workflow, collaboration diagram (click to view full size).
- Mapped end-to-end workflows for an AI interview platform across 4 stages, wrote functional specs, and prototyped real-time voice sessions to align technical delivery with business objectives.
View example artifact: process flow (BPMN-style) ▶
Illustrative reconstruction for portfolio purposes: client-confidential values generalized; structure and role reflect the actual engagement.
- Elicited requirements from non-technical coaching staff, translating operational needs into functional specs and a scheduling, attendance, and CSV-reporting platform, cutting manual work by ~2–3 hours/week.
View example artifact: scheduling workflow ▶
Illustrative reconstruction for portfolio purposes, reflecting the actual delivered workflow.
- Gathered user requirements, documented RESTful API specs for a VOD platform (MyVioo), and contributed to sprint planning and functional validation within a 2-week Agile cycle.
View example artifact: API contract excerpt ▶
| Method | Endpoint | Description |
|---|---|---|
| GET | /videos | List catalog videos with pagination and category filter |
| GET | /videos/{id}/stream | Return signed stream URL for a given title |
| POST | /users/{id}/watchlist | Add a video to a user's watchlist |
Illustrative reconstruction for portfolio purposes, reflecting the actual documented contract.
A self-directed, solo-founder BA engagement: running the full requirements-to-architecture lifecycle on an original product, the same discipline practiced at Wavess, but fully public and updated as each phase lands. Differentiated by real-time per-muscle volume tracking (MEV/MAV/MRV) and multi-method progressive overload detection, benchmarked against Hevy, Strong, Fitbod, and Tracked.
View artifacts: Charter, Elicitation Plan & Gap Analysis ▶
| Artifact | Key output |
|---|---|
| Charter | Business objective, scope (in/out), success criteria: artifacts must be handoff-ready and open questions must be surfaced, not silently resolved. |
| Elicitation Plan | 3 stakeholder groups mapped by power/interest; techniques matched per group (competitor-review mining, screened surveys, technical feasibility workshops). |
| Gap Analysis | Benchmarked against Hevy, Strong, Fitbod & Tracked. Confirmed real-time per-muscle volume tracking as the strongest surviving differentiator once overstated claims were corrected. |
14+ open questions logged and tracked across phases rather than resolved by assumption, each tied to the phase and decision-owner that will close it.
View real process models: P1 Live Workout Session & P2 Authentication ▶
6 processes modeled at Level 1–2 (P0–P5) from structured BPMN specs: pools, lanes, gateways, and business rules written out before drawing. Two are shown below.
P1: Live Workout Session Logging. The core differentiator loop: set entry → RIR-weighted volume calculation → real-time feedback → overload detection (click to view full size).
P2: Authentication. Session/token validation, OAuth and email/password paths, first-time vs. returning-user routing (click to view full size).
- Requirements Engineering (RTM, NFRs)
- BPMN (As-Is / To-Be)
- User Stories & Acceptance Criteria
- Backlog Prioritization
- Stakeholder Elicitation
- UAT · Gap Analysis · UML
- API Contract Design
- Data Modeling & ERDs
- Bounded-Context Analysis
- Architectural Trade-off Analysis
- SQL
- LLM / RAG Pipelines
- Vector Databases
- Prompt Engineering
- AI-Assisted Analysis & Documentation
- Jira · Camunda Modeler · Draw.io
- Postman
- Supabase · PostgreSQL · Redis