u/Individual-Bench4448

I've been working on Ailoitte, an AI-native product company, and we recently structured our core offering into what we call AI Velocity Pods, 12-week, outcome-based engagements where we take a startup's AI use case from brief to production.

The pitch is simple: MVP in 4 weeks, full engagement in 12, complete IP transfer on delivery, no hourly billing.

We built this because the pattern kept repeating. Startups had the idea, the budget, and the urgency, but kept getting stuck with vendors who billed hours and delivered Notion docs.

What I'm genuinely trying to figure out:

Does the outcome-based positioning actually land, or does it sound like every other agency claiming they're "different"?

Is 12 weeks credible for production-ready AI, or does it trigger skepticism immediately?

Product page here if you want context → https://www.indiehackers.com/product/ailoitte

Not looking for upvotes, looking for the kind of feedback that stings a little.

Curious if anyone here has actually done this.

We're evaluating how we structure our next build phase, and outcome-based pricing keeps coming up as an option: pay for what gets delivered, not hours logged.

sounds good in theory, but every time we get into specifics, it quietly becomes a retainer with milestone labels.

Has anyone at an early-stage startup actually made this work? What did the success metric look like? And what happened when something slipped?

Not looking for recommendations, just want to know if this is a real model or mostly pitch deck language at this point.

Genuinely trying to understand this distinction better.

From the outside, "generative AI development" and "software development with AI tools" can look identical; both involve LLMs, both produce software, and both use similar stacks.

But I've seen these treated as very different things in job listings, vendor categories, and even team structures.

My current understanding: generative AI development means the AI output is part of the product itself (text generation, code generation, retrieval, agents), while AI-assisted development means AI helps the developer build faster, but the output is still traditional software.

Is that the right way to think about it? Or is the line blurrier than that?

Asking because I'm trying to map out what skills and workflows actually matter for each.

A lot of AI tooling vendors and engineering shops are throwing around numbers like "3x faster" or "5x faster delivery."

skeptical by default, but also open to being wrong.

So genuinely asking, has anyone worked in an environment where AI tooling or AI-native processes produced a measurable, significant speed increase in actual software delivery? not just autocomplete satisfaction, but real reduction in time from spec to production.

If yes, what was the stack? What was the workflow? and what would you say actually drove the improvement?

if the answer is no, what do you think the realistic ceiling is for AI-assisted delivery improvement, given current tooling?

been watching the ML hiring market from an employer perspective for the past year. wanted to share what's actually happening because I think it's relevant if you're job searching in this space.

the numbers:

• us senior ML engineers: $250k-$350k total comp
• offshore equivalents (India, pre-vetted, production experience): $38k-$80k
• 1.5m unfilled US software positions through 2028

What's happening: Series A-B companies literally cannot compete with FAANG for the same candidates. They're losing final-round candidates to bigger companies consistently. Many are moving to offshore AI engineering teams as a result.

Why this matters for job seekers:

If you're a US-based ML engineer and wondering why some companies ghost after final rounds, it's often because the budget math breaks down when a FAANG offer arrives.

If you're considering offshore opportunities, the market rate is rising as FAANG scales India operations. The spread between us and offshore is compressing.

For engineers on either side, what's your read on how this plays out over the next 18 months as FAANG continues scaling globally?

sharing the exact components that moved accuracy from 62% to 94% on a production rag system. all langchain.

SemanticChunker (langchain_experimental) — swap out RecursiveCharacterTextSplitter. breakpoint_threshold_type="percentile", start at 85 and tune per doc type.

EnsembleRetriever — bm25 + vector, weights [0.4, 0.6]. weights matter less than you'd think if you're reranking after.

CrossEncoderReranker + ContextualCompressionRetriever — cross-encoder/ms-marco-MiniLM-L-6-v2. adds ~280ms. worth it if accuracy > latency for your use case.

metadata filtering — source_authority field on every doc (1=primary, 2=secondary). filter in retrieval to prefer primary sources when there's a conflict. boring, high impact.

No model changes throughout. Everything above is retrieval-side.

open question: has anyone built query routing in LangChain to skip reranking on simple single-doc queries? trying to avoid the latency cost on queries that don't need it.

been debugging a production rag system for the past few months. wanted to share what actually moved accuracy vs what didn't.

things that didn't help: prompt engineering, bigger chunks, switching embedding models.

things that did:

semantic chunking over fixed-window — biggest single change, especially on multi-page docs where logical structure doesn't respect token boundaries.

hybrid search (vector + bm25 with rrf) — vector alone was missing exact-match queries. regulation codes, internal identifiers, versioned names. adding bm25 and fusing with reciprocal rank fusion fixed this category almost entirely.

cross-encoder reranking — adds latency but the top-k by similarity isn't the same as top-k by relevance to the actual question.

eval suite first — 150 real user queries with reference answers, ragas grading. without this none of the above is measurable.

no model changes throughout. same llm, same prompt, same temp.

anyone running hyde for query expansion in production? benchmarks look good but curious about real-world results on domain-specific workloads.

Early on, we scoped MVPs the same way most teams do: list the features, estimate hours, and add a buffer.

It was wrong almost every time. Not because of scope creep or bad engineers, but because AI products have a fundamentally different risk profile from traditional software.

The features that look small on a spec sheet (hallucination mitigation, latency tuning, context window management) can eat a week each. The ones that look hard (the AI model itself) are often the easy part.

So we rebuilt our scoping model around time-boxed outcome gates instead:

Week 1: Data pipeline + baseline model integrated. Working end-to-end, even if it's rough.
Week 2: Evaluation framework live. We know what "good" looks like and can measure against it.
Week 3: Integration layer complete. The AI is talking to the real system, not a mock.
Week 4: Performance targets met. Latency within budget. Hallucination rate within an acceptable range. Ship-ready.

This forces hard conversations about scope in week 1 instead of week 4. It also means the client sees real progress every week, not a demo that falls apart in staging.

What does your MVP scoping process look like? Do you estimate by time or by output?

We're r/Ailoitte. We've been building software since 2017, and we've never sent a client a single hourly invoice.

Not once. Not a single "here's what we burned this week" bill. Every engagement we've ever run has been fixed-price, milestone-gated, and outcome-defined before a line of code was written.

That's not a sales pitch. It's just the only model we know how to run.

We built this subreddit to talk openly about what outcome-based engineering actually means in practice, the mechanics, the tradeoffs, and the parts that are harder than they look.

What outcome-based engineering means at Ailoitte:

Every project runs through what we call an r/AIVelocityPods, a scoped team activated within 48 hours of contract sign-off. The deliverable is defined upfront. Payments are released upon milestone acceptance, not upon time elapsed. If a sprint overruns, we absorb it. If the architecture needs a rethink in week two, that's our problem to solve at the agreed price. Full IP transfers to you on completion.

300+ products shipped across 22 countries under this model. Every single one at a fixed price.

What we want to talk about here:

— Why T&M billing structurally misaligns incentives between firms and clients
— How fixed-price contracts actually work at scale (and where they break down)
— The role AI tooling plays in making outcome-based delivery financially viable
— Real case studies: 50M+ user platforms, HIPAA-regulated systems, B2B at scale
— The hard questions, because some projects shouldn't be fixed-price, and we'll say that too

We're not here to post announcements every week. We're here because this model deserves a real conversation.

What do you actually want to know about how this works?

We've been tracking failure points across the AI MVPs we've scoped and built over the last year, and there's a pattern that shows up almost every time.

Weeks 1 and 2 feel fine. The team is moving. The demo looks good. Everyone's aligned.

Then week 3 hits, and things start to silently fall apart.

Here's what we keep finding at the root:

1. The integration layer was never properly scoped. The model works. The API works. But nobody mapped how they'd actually talk to each other in production. This alone delays 70% of the MVPs we've rescued.

2. Evaluation was deferred. Nobody set up a way to measure whether the AI was actually doing what it was supposed to do. You can't course-correct what you're not measuring.

3. The data assumptions were wrong. What was available in dev didn't look like what existed in production. This usually surfaces in week 3, right when it hurts most.

We restructured our 4-week MVP process specifically around these three failure modes. Each week now has a checkpoint that catches each one before it compounds.

Curious whether others building AI products have hit similar walls.
What broke for you, and did you see it coming?