Model Failure Diagnostic

Most teams commit to an intervention before they know what’s failing. The diagnostic starts one step earlier.

$3–5k · fixed scope · 2 weeks

Most teams pick an intervention — fine-tuning, RAG refactor, DPO alignment, and then build evals to measure it. That’s backwards. The failure modes in your production data determine which intervention has a chance of closing the gap. The diagnostic maps that sequence before you commit.

In two weeks, we analyze your production failures across the full stack, retrieval, weights, eval coverage, and classify which failure modes are systematic. The output is a written report you own outright: failure mode analysis, prioritized interventions, eval design, and build specification, whether you build with us, another team, or in-house.

Book a free call →

Free 30-minute intro call. No commitment. We work under NDA.

Why the diagnostic first

The intervention decision is easy to get wrong without a diagnosis.

The failure modes in your production data should determine the intervention, not what you’ve tried before or what a vendor recommended. A RAG refactor on a problem in the model weights, or a LoRA adapter for a task that needed better retrieval: both are failures of sequence, not execution.

Every AI services company has a specialty. Fine-tuning shops recommend fine-tuning. RAG consultancies recommend RAG. The recommendation follows from their default, not your production failures.

At $3–5k, the diagnostic classifies which failure modes are actually present, maps each one to the right intervention, and defines what it will cost, before any build commitment. You go into the execution decision with a written analysis, not a guess made before anyone’s seen your data.

The first mistake is usually the technique. The second is the evals built around it.

What you get

A written report you own outright, regardless of what you do next.

After two weeks, you’ll have a clear picture of what’s failing, why, and what it will cost to fix it, with enough specificity to act on with us, another team, or in-house.

A verdict on what’s failing and why

Your production failures classified by failure mode and linked to the interventions most likely to close each one. Prioritized by expected impact. Why the top recommendation fits your specific failure pattern, and why the alternatives were ruled out. A document your team can read, challenge, and act on without us in the room.

Where in the stack the problem lives

A systematic breakdown of where and why your current approach is failing: whether the bottleneck is in retrieval, model weights, pipeline design, or data quality. The diagnosis distinguishes a retrieval problem solvable with chunking or embedding changes from a model-layer problem that requires training signal.

Eval design with defined acceptance thresholds

No eval plan means no way to know if you’re done. This deliverable defines it: eval set structure, benchmark approach, acceptance thresholds, agreed before execution starts. You verify the build against it, not against how it feels.

Full cost and timeline before you commit

Cost ranges for your problem type and data volume: training compute, infrastructure requirements, and calendar time per intervention. What it will cost, how long it will take, and what the intervention is designed to address, agreed before any build commitment.

Book a free call →

Free 30-minute intro call. No commitment.

How it works

Two weeks. Three stages. One written report.

Data and pipeline review

We review your training data, current pipeline architecture, and production failure patterns. We identify what’s failing, how often, and under what conditions. This establishes the failure profile before any technique is considered.

Failure classification and intervention mapping

We map each failure mode to the intervention most likely to close it (retrieval hardening, LoRA/QLoRA, full fine-tune, DPO alignment, compression) and determine where in the stack the fix lives. We document alternatives considered, why they were ruled out, and cost and trade-offs for each recommendation.

Written report

A structured document with: technique recommendation, decision rationale, alternatives considered, root cause analysis, evaluation plan with acceptance criteria, compute cost estimates, and build timeline. You own it outright. Take it to your internal team, another vendor, or use it to scope a Baseweight execution engagement, no obligation either way.

Questions about the diagnostic

Common questions

The intro call (30 minutes, free) is a scoping conversation: we assess whether the diagnostic is the right next step and what it would cover for your specific situation. The diagnostic is two weeks of technical work: systematic failure analysis, stack-wide assessment, and a written deliverable. The call comes first; the diagnostic follows if it’s the right fit.

We’ll say so. If better prompting or a minor RAG adjustment closes the gap, that’s the recommendation, even if it means no further engagement. Recommending unnecessary work would undermine the independence the diagnostic is designed to provide.

The API call is easy. What you get back is a training loss curve, not a verdict on whether you fixed the specific failure modes that were breaking production, or whether you regressed on cases you didn’t include in training. You need evals calibrated to your production distribution to answer those questions. Building those is most of what the diagnostic produces.

Eval platforms measure what you configure, accurately. The assumption baked in: you already know which failure modes to track. If your eval coverage doesn’t match your production failure distribution, you get accurate measurements of the wrong things. The diagnostic defines what to measure. Your existing platform can then measure it.

We work under NDA and can operate within your infrastructure. For the diagnostic, we need access to representative failure cases and pipeline architecture — not necessarily your full training set. Data handling requirements are agreed before any access begins.

Yes. But execution without a prior failure analysis is how teams end up retrofitting after a failed attempt. The diagnostic is the faster path to a working build, not a gatekeeping step. If you’ve already run a thorough internal diagnosis and know exactly what you need, we can scope execution directly on the intro call.

The diagnostic report becomes the scope document for execution. Execution pricing is defined in the report, so you go into a build decision with full information. The report is yours outright: use it with us, take it to another team, or execute in-house. It was written to be actionable regardless of what comes next.

Because the failure mode you were targeting was never defined. You ran a training job: the provider returned a loss curve, you deployed, the problem was still there. The diagnostic starts a step earlier: classifying which failure modes are actually in your production data before any training code runs. Fine-tuning without that step is common. It’s also how teams spend months on the wrong fix.

Get started

If you’ve been patching the same problem for two sprints and the gap still isn’t closing: that’s what the diagnostic is for.

Book an intro call to walk through your failure patterns, where performance is breaking down, and whether the diagnostic is the right next step. 30 minutes. Free. No commitment.

Book a free call →

After you book, we’ll send 3 questions so we can focus the 30 minutes on your specific situation.

Prefer email? phil@baseweight.co · We work under NDA.

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