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Code reviewArch. reviewArch. design

0→1 founder-CTO for data-heavy SaaS and agent-ready tooling

Best for: solo founders and seed startups building a data or AI product · SaaS teams adding an API or MCP server · developer-tool companies whose CLI or API will be called by AI agents · founders who want product and engineering advice in one person.

founder-CTO / founding product engineer
B2B market intelligence and data products, mobile app ecosystem, real estate search
Python, TypeScript, FastAPI, SQL (MySQL, PostgreSQL), ClickHouse, Elasticsearch, Redis, Celery / RabbitMQ
Europe
[ M · 01 ]
11
Track-record cases
[ M · 02 ]
1
Scoped reviews you can book with this expert
[ M · 03 ]
6/ 6
Review and architecture services offered
[ M · 04 ]
4
Buyer needs this expert is matched to

§ 01Snapshot

§ 02Services

✓PR/MR code reviewPython, TypeScript, agent tooling
✓Architecture reviewdata pipelines, SaaS, RAG / MCP
✓Architecture design0→1 SaaS, ingestion + search, AI document processing
✓Audit / due diligenceagent-readiness of CLIs, APIs and MCP servers
✓Vibe-code rescueAI-built Python / TypeScript MVPs
✓Team mentoringmulti-agent coding setups, small product teams

§ 03Track record

Bootstrapping a B2B market-intelligence SaaS

One of our experts shipped the first version of a market-intelligence SaaS for the mobile app ecosystem alone, up to first revenue, then grew the team from one to about ten. They owned product, pricing and engineering. The product reached well over a hundred thousand monthly users and around two thousand paying customers.

B2B SaaS / mobile app market data · bootstrapped, team of about tenPython, FastAPI, MySQL, ClickHouse, Elasticsearch, Redis

Data pipelines at millions of records per day

The same SaaS tracked millions of apps: installs, revenue estimates, rankings, SDK usage, store-page changes. Our expert designed pipelines that process millions of records daily, with queues and workers for ingestion, a columnar store for analytics and a search engine for lookups. This powers search, reports and customer-facing analytics.

B2B data / analytics · small SaaS teamPython, Celery / RabbitMQ, ClickHouse, Elasticsearch, MySQL, Redis

Natural-language questions over market datasets

Customers had to learn dozens of filters to get answers from a complex dataset. Our expert added natural-language querying on top of the existing analytics stores, using LLMs and LangChain to turn questions into queries. Users can ask in plain language instead of building filters.

B2B data / analytics · small SaaS teamPython, LangChain, OpenAI API, ClickHouse, Elasticsearch

A metered public API for a data product

Business customers wanted the data inside their own tools, not in a dashboard. The expert's product exposed a REST API with access tokens and per-call credits on the top plan, plus published docs and examples. Data use moved into customer workflows and the API became a separate paid offering.

B2B data / developer APIs · small SaaS teamPython, FastAPI, REST, OpenAPI docs, usage metering

A listings aggregator built on crawling and technical SEO

In a fragmented regional market, listings were scattered across many agency sites. Our expert built crawlers and an indexing engine that normalise and deduplicate listings from many sources into one search with faceted navigation. Technical SEO and a fast UX made it one of the leading searches in its market.

real estate / vertical search · founder-led startupPython crawlers, normalisation and dedup pipelines, search index, SEO-friendly page structure

Growth data pipelines across a product studio's launches

A product studio launched many web and mobile products and needed to know which ones paid back. Our expert built automated pipelines that connect ad spend, user behaviour and lifetime value, so spend could follow profit. Across launches the studio generated more than ten million installs and leads.

consumer web and mobile apps, lead generation · small product studioPython, SQL, marketing and analytics APIs, ETL

Early high-load back end for a consumer social product

As an early engineer on a consumer social product, our expert built core back-end systems, the user-profile architecture and queue-based message processing. The product later grew to a very large user base.

consumer social · early-stage teamback-end services, message queues, high-load profile storage

Scanned agreements → structured reporting schedules

Accountants were re-keying lease agreements by hand to produce reporting schedules. As founding engineer, our expert designed a system that ingests scanned documents, reads them and turns them into reporting schedules, and shipped it quickly with agentic coding.

accounting / finance automation · seed-stage AI startupPython, OCR, LLM extraction, Claude Code, Codex

Publisher content as a citable AI expert, with MCP attribution

Publishers' knowledge was being used in AI answers without credit or measurement. As founding engineer, our expert built a system that discovers and ingests site content, extracts and encodes knowledge, and serves it through an MCP server with natural-language retrieval. They added end-to-end attribution from the MCP tool call to the resulting click, view and site event.

AI infrastructure / media · seed-stage startupPython, RAG, MCP server, event tracking, Claude Code / Codex

Agent-readiness review of CLIs, APIs and MCP servers

AI agents call CLIs and APIs constantly, and many tools hang on prompts, return ambiguous exit codes or emit unparseable text. Our expert tests tools the way an agent uses them and reviews them against a practical checklist: non-interactive mode, retry-safe and side-effect-aware error semantics, one machine-readable response shape, input validation and least-privilege credentials.

developer tools / AI agents · any tool vendorPython, JSON Schema, conformance tests

Multi-agent coding pipeline with real tests

Two early-stage products had to ship fast with a very small team. Our expert ran several coding agents that write, review each other, ship and deploy, while the expert owned decomposition and tests. They also evaluated a commercial agentic IDE for about a week against senior-engineer practice and found that it skipped tests, padded scope and marked crashed tasks as done. Their fixes: smaller tasks, separate planning sessions, and raising "not implemented" instead of fake success.

AI-native startups · seedClaude Code, Codex, Python, CI/CD

§ 04What you can book

Review of an AI-built SaaS before it takes payments

A founder has a Python or TypeScript SaaS built mostly with AI tools and is about to add billing, an API or an MCP server. The expert reviews data model and pipeline design, which store serves which query, metering and pricing hooks, retry and idempotency on anything that writes, and whether the interfaces are agent-safe.

You get

A ranked fix list, a target architecture sketch and the first fixes made together with the team.

Vibe-code rescue · Architecture reviewPython / FastAPI or TypeScript, PostgreSQL / MySQL, ClickHouse or Elasticsearch when relevant, MCP

Questions buyers ask

Why do AI agents fail when they call command-line tools?+
Most CLIs were designed for humans. They page output and wait for a keypress, return the same exit code for "nothing happened" and "half-done", print free text instead of structured data, or crash silently on odd input. An agent then stalls, wastes tokens, or retries a non-idempotent action. A review checks exit codes, output format, timeouts and retry safety against a list of known failure modes.
What makes an API or MCP server "agent-ready"?+
Stable machine-readable errors, a single structured response shape, explicit statements of whether a call is safe to retry and whether it had side effects, and a way for the agent to learn every command and parameter in one call. Without these, agents fall back to trial and error.
How should a small SaaS team structure a high-volume data pipeline?+
Separate ingestion (crawlers, queues, workers) from storage built for the query pattern: a row store for transactions, a columnar store such as ClickHouse for analytics, and a search engine for lookup. One of our experts ran this pattern at millions of records per day with a team of about ten.
Can coding agents review each other's work?+
Yes, as one layer. One expert in our network runs a setup where several coding agents write, review, ship and deploy, with a human owning the architecture and the tests. Agents that grade themselves tend to report success on unfinished work, so independent review and real tests are what make it safe.
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How it works

  1. Tell us what you need — the repo or system, the question, and the deadline.
  2. We match an expert from the network, with a second reviewer where it helps.
  3. Scoped work, contracted through Invental — review per pull request, a fixed-scope audit or architecture review, or ongoing capacity.

— Invental · software studio · Montevideo, UY

Want this expert on your code?

Tell us what you are building and what you want checked. We confirm the match and the scope before anything starts.

— Get in touch
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A link or short description of the code or system, what you want checked, your stack, and when you need the answer. No repository access is needed until scope is agreed.

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