How a Seed-Stage AI Startup Built Its RAG Pipeline in 30 Days Offshore

Company snapshot

Every Remoteria engagement starts with a clear picture of the company we are working with — headcount, revenue stage, geography, and the specific pressure that triggered the outreach. Here is the profile of the composite company represented in this case study.

Type

B2B AI SaaS (document intelligence)

Size

5 employees

Location

San Francisco, CA

Stage

Seed stage, YC-adjacent

Seats placed

3 offshore seats

Tags

ai-startup · rag · ml · seed-stage

The challenge

Most Remoteria engagements begin with a specific pressure point — a runway concern, a production bottleneck, a response-time problem, or a deadline that cannot slip. This one was no exception. The best way to read any case study on this site is to start with the pressure. If you recognize the pressure, the rest of the story will tell you whether the approach we took would fit your team. If you do not recognize the pressure, there is probably a different case study in our hub that maps more cleanly to your situation.

The founders had closed their seed round four months earlier and had committed to a specific investor demo 30 days out. The demo needed a working retrieval-augmented generation pipeline that could answer questions against a customer-uploaded document corpus with reasonable accuracy. Their existing two-engineer team was fully committed to the customer-facing web app. They had interviewed four local ML engineers in San Francisco and every one had quoted $180K+ base plus equity for a full-time role that would not even start for six weeks. They needed a senior team that could start Monday, ship in a month, and not consume half their seed round doing it.

What the company did next is what most companies under this kind of pressure do not do: rather than reaching for more of the same headcount at the same cost, the decision maker chose to pause and rethink the shape of the team before adding to it. That single decision is usually the difference between a company that scales through an inflection point and a company that grinds to a halt at it.

The solution

We scoped the work with the company’s decision maker, mapped it to a specific number of offshore seats, and ran a shortlist-and-hire process designed to get the right people in seats inside of two weeks. Here is how the engagement ran. The structure matters because it is reproducible. Every engagement we run follows the same rough pattern: scope, shortlist, sign, onboard, measure. We rarely deviate from the pattern, and the reasons we do not are usually specific to the industry rather than to the individual company.

We scoped the build with their technical founder over a single working call. The target architecture was LangChain for orchestration, Pinecone for the vector store, OpenAI embeddings plus GPT-4 for generation, and a FastAPI layer on top. We mapped the work to three seats: a senior AI Agent Developer to own the LangChain pipeline and prompt engineering, a mid-level Machine Learning Engineer to own the eval harness and fine-tuning experiments, and a senior Full Stack Developer to build the FastAPI layer, the Postgres metadata store, and the customer-facing upload UI. All three were signed within seven days of kickoff. Onboarding was compressed. Day one was a deep review of the existing codebase and a whiteboarding session on the target architecture. Days two through five the team built the first end-to-end path: document upload, chunking, embedding, vector storage, retrieval, and generation. Days six through 12 were iteration on chunking strategy and retrieval quality against a 200-question evaluation set the founder had built from customer interviews. Days 13 through 20 were prompt tuning, eval harness improvements, and first round of fine-tuning on a smaller Llama model as a fallback path. Days 21 through 27 were the FastAPI layer, the upload UI, and pre-demo hardening. The pipeline shipped on day 27, three days ahead of schedule, with 94% accuracy on the internal eval set.

A note on how we vet. Every candidate on a Remoteria shortlist has already shipped production work for a US or European client in their specialty, passes a role-specific take-home or work sample, and walks the hiring manager through a past project in the final interview. We reject roughly nine out of every ten candidates who apply to our talent pool. The one in ten who make it through are the profiles that end up on engagement shortlists like the one described in this case study.

The results

Results are measured against the pre-engagement baseline and reported across the first 90 days of full production unless otherwise noted. Figures are representative of typical outcomes across Remoteria engagements in the ai / ml startups segment.

What they said

The following quote is a composite, assembled from the phrasing and sentiment we consistently hear from clients in the ai / ml startups segment at the end of a 90-day engagement. It is not attributable to a single named individual.

“Four weeks out from the demo I was ready to push the investor meeting a month. A friend told me about Remoteria at the right time. Seven days later we had three senior engineers starting Monday, and 27 days after that we were running a live RAG demo in front of our lead. The round closed ten weeks later. I do not know what our cap table would look like if we had tried to hire locally.”

Roles we placed

This engagement placed 3 offshore seats across the following roles. Each link goes to the role hub where you can see starting price, typical responsibilities, and the profile of a pre-vetted candidate in that seat.

• Hire AI Agent Developers
  From $3500/month · Pre-vetted · 2-week onboarding
• Hire Machine Learning Engineers
  From $4000/month · Pre-vetted · 2-week onboarding
• Hire Full Stack Developers
  From $3000/month · Pre-vetted · 2-week onboarding

Key lessons from this engagement

Every engagement teaches us something about what works and what does not in the specific industry we are working with. Here are the three takeaways we would bring forward to any future company in a similar situation.

1. Lesson 1

   For a time-boxed technical build, a senior offshore pod will outperform a local junior hire every time. The three engineers on this pod had all shipped LangChain + Pinecone production code for US clients in the previous 12 months. That prior context is what made the 27-day ship possible.

2. Lesson 2

   Build the eval harness before you build the pipeline. The founder had already written 200 questions and expected answers before we started, which meant the team could measure accuracy on day two instead of day twenty. Every improvement after that was measurable.

3. Lesson 3

   Do not over-engineer the first version. The team shipped with off-the-shelf OpenAI embeddings and GPT-4, not a custom fine-tuned model. The fine-tuning experiments ran in parallel as a fallback path but were not on the critical path for the demo. Ship the boring version first.

Considering a similar engagement?

If you recognize your company in this story — a similar size, a similar stage, a similar pressure point — we would be glad to walk you through what a comparable engagement would look like for your team. The first call is 15 minutes and costs nothing. Come in with the role you are trying to fill, your rough budget, and the timezone you want overlap in. We will send three pre-vetted candidate profiles within five business days of the call.