AI Agent & SaaS Engineer for Product Teams
AI Agent & SaaS Engineer for Product Teams
Bridging the gap between design and development
Bridging the gap between design and development
Senior Framer + Figma Partner for SaaS & Founder-Led Brands
Senior Framer + Figma Partner for SaaS & Founder-Led Brands
Building time series forecasting pipelines
Scout: AI-assisted forecast research โ Quantura
I built Scout inside Quantura to help users interrogate the history and forecast they are actually viewing. Three contextual question cards organize the experience around Forecast, History and Evidence.
Users can inspect median changes and uncertainty ranges, ask about observed highs and lows, and review data sources and units. Structured answers use the selected observations and forecast values as context.
The interface also supports chart annotations, user notes, saved conversations, and named CSV previews before forecasting. The goal is to make a forecast easier to inspect, explain and revisit.
My work combines TypeScript backend logic, a JavaScript research interface and interactive charting.
Explore: https://quantura.studio/forecasting
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Forecast API, SDKs & OAuth MCP โ Quantura
I built a shared Search โ Download โ Forecast API for Quantura so developers can move from a discovered market or dataset to historical observations and asynchronous forecast results.
The integration includes JSON/CSV exports, job status handling, provider and model metadata, scoped API access, and OAuth sign-in for MCP clients. Python and TypeScript SDKs plus a CLI provide reusable integration examples.
I also organized the public RapidAPI listing into clear endpoint groups, documented usage and authentication, and added examples for scheduled forecasting workflows.
This work is useful for teams connecting forecasts to their own apps, notebooks or AI tools.
Explore the API: https://rapidapi.com/tamzid2001/api/quantura2
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Forecasting pipelines across market and economic data
Built as part of Quantura, my forecasting and research product. I implemented a shared data workflow for Alpaca, Dukascopy, Gemini, Treasury Fiscal Data, World Bank Data360 and BigQuery public datasets.
The work covers provider discovery, series selection, timestamp and unit normalization, observed-history downloads, six forecast quantiles and scheduled execution. Data coverage and model requirements remain explicit, and missing observations are not invented.
Deliverables include Python/TypeScript integrations, JSON/CSV exports, asynchronous job handling and chronological historical evaluation.
I can build a similar workflow for your supported time-series data, with clear inputs, milestones and operating costs.
Explore: https://quantura.studio/
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Software Engineer skilled in web and mobile development.
Avid Software Engineer, Data Engineer and Technical Writer
Data, AI, Software, Technical Writing
๐ฌ๐ข๐จ๐ฅ ๐ฆ๐๐๐๐ฆ ๐๐๐ง๐ ๐๐๐ฅ๐๐๐๐ฌ ๐๐ก๐ข๐ช๐ฆ ๐ช๐๐๐๐ ๐๐จ๐ฆ๐ง๐ข๐ ๐๐ฅ๐ฆ ๐๐ฅ๐ ๐๐๐ข๐จ๐ง ๐ง๐ข ๐๐๐๐ฉ๐.
Most owners check that money came in. Few ask what the numbers say about next month.
I ran a 90-day Dead Stock Check on Horti Supplies data (about $14,500 in sales).
In plain sight:
๐ค ๐ฒ ๐ผ๐ณ ๐๐ต๐ฒ ๐๐ผ๐ฝ ๐ฎ๐ฑ ๐ฐ๐๐๐๐ผ๐บ๐ฒ๐ฟ๐ ๐พ๐๐ถ๐ฒ๐๐น๐ ๐๐๐ผ๐ฝ๐ฝ๐ฒ๐ฑ ๐ฏ๐๐๐ถ๐ป๐ด: about $550 a month walking away.
$๐ต๐ฐ๐ฌ ๐๐๐๐ฐ๐ธ ๐ผ๐ป ๐๐ต๐ฒ ๐๐ต๐ฒ๐น๐ณ - 8 products unsold for 30 to 83 days made 2.4% of sales but held 22% of stock value.
๐ ๐ง๐ต๐ฒ ๐ฏ๐ฟ๐ฑ ๐ฏ๐ฒ๐๐-๐๐ฒ๐น๐น๐ถ๐ป๐ด ๐ฝ๐ฟ๐ผ๐ฑ๐๐ฐ๐ ๐ฟ๐ฎ๐ป๐ธ๐ฒ๐ฑ ๐ฎ๐ญ๐๐ ๐ณ๐ผ๐ฟ ๐ฝ๐ฟ๐ผ๐ณ๐ถ๐.
๐ ๐ง๐ต๐ฟ๐ฒ๐ฒ ๐๐น๐ผ๐ ๐ฎ๐ณ๐๐ฒ๐ฟ๐ป๐ผ๐ผ๐ป ๐ต๐ผ๐๐ฟ๐ - brought in 7% of sales; rent and staff costs didnโt drop.
๐ง๐ฟ๐ ๐๐ต๐ถ๐ ๐๐ผ๐ฑ๐ฎ๐ (๐ญ๐ฌ ๐บ๐ถ๐ป๐๐๐ฒ๐, ๐ป๐ผ ๐๐ผ๐ณ๐๐๐ฎ๐ฟ๐ฒ): export last monthโs sales, sort by profit, and circle anything that earned almost nothing but took up shelf space.
๐ง๐๐ผ ๐๐ฎ๐๐ ๐ ๐ฐ๐ฎ๐ป ๐ต๐ฒ๐น๐ฝ:
1๏ธโฃ ๐ง๐ต๐ฒ ๐๐ฒ๐ฎ๐ฑ ๐ฆ๐๐ผ๐ฐ๐ธ ๐๐ต๐ฒ๐ฐ๐ธ: send one month of sales data (names removed is fine) and get a one-page report on where cash is stuck, which customers are drifting, and 3 actions for this week. ๐ The first 10 get it free, in return for sharing the anonymised result as a case study.
2๏ธโฃ ๐๐ผ๐บ๐ถ๐ป๐ด ๐๐ผ๐ผ๐ป, ๐๐ป๐๐ถ๐ด๐ต๐๐๐ผ๐ผ๐ฝ: upload a CSV or SQL export and get a named diagnosis (Customer Churn, Dead Stock, Demand Forecast) plus a visual report.
๐๐ผ๐บ๐บ๐ฒ๐ป๐ โ๐๐๐๐๐โ ๐ณ๐ผ๐ฟ ๐ฎ ๐ณ๐ฟ๐ฒ๐ฒ ๐๐ฒ๐ฎ๐ฑ ๐ฆ๐๐ผ๐ฐ๐ธ ๐๐ต๐ฒ๐ฐ๐ธ, ๐ผ๐ฟ โ๐๐ข๐ข๐ฃโ ๐ณ๐ผ๐ฟ ๐๐ต๐ฒ ๐๐ป๐๐ถ๐ด๐ต๐๐๐ผ๐ผ๐ฝ ๐๐ฎ๐ถ๐๐น๐ถ๐๐.
Iโm Moses, a data engineer and founder of Closertune. Do you know an owner who says, โSales are slow, but I donโt know whyโ?
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I recently built an interactive Power BI sales dashboard tracking $17.91M in total sales across supervisors, managers, and order years.
I then designed the data model and DAX measures behind the KPI cards, gauge, and trend visuals, and built drill-down views: sales by supervisor and year, quantity split by manager, and order volume by year.
I used combo visuals (donut, gauge, bar, and line charts) on one page so stakeholders can get the full picture without switching reports.
I also focused on a clean, single-screen layout that non-technical stakeholders can read at a glance.
#Microsoft Power BI #Daxko Operations #Data Visualization #Data Modeling #Business Intelligence
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Building the technical foundation for Otonix, an AI-native IT Operations Platform
I worked on the engineering side of Otonix, an autonomous IT operations platform built on agent-first, task-specific AI agents that monitor, triage, remediate, and escalate IT operations, instead of a chatbot bolted onto legacy RMM/PSA tooling.
What the build involved:
๐น Architecting the platform around four core agent capabilities: Monitor, Triage, Remediate, and Escalate.
๐น Building the policy-guardrail layer that constrains automated actions and gates anything touching regulated systems or irreversible changes.
๐น Implementing the audit trail: a signed, searchable, exportable log of every agent decision and action.
๐น Working with a signal-ingestion pipeline (endpoints, servers, network, IoT) feeding into the triage/remediation flow.
Python/FastAPI, Node.js, React, PostgreSQL, Docker, n8n, Kubernetes
I build backend systems and full-stack platforms for technical products. Especially where reliability, auditability, and guardrails matter as much as the automation itself. If you're building something in a similar space, let's talk.
๐ฉ mosesmorrisdev@gmail.com | github.com/Moses-Morris
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Designs and builds websites, web apps and AI tools, NYC
NovaPulse Agent, our in-house autonomous AI agent. A console where a user hands over a mission (web research, code in a sandbox, strategic audit) and the agent plans, calls its tools and reports back. Built on the eve framework in TypeScript with Claude as the reasoning model, Tavily for search and Resend for email, with guardrails: authenticated access, SSRF protection, an email kill-switch, a server-side stop and an eval harness. Deployed on Vercel.
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