We run a 2-week diagnostic on your pipeline, data, and quality gaps, prescribe the right technique—RAG refactor, LoRA fine-tune, DPO, or another approach—and build it. You keep the weights.
30 minutes. No cost, no commitment. We'll tell you what's wrong with your current approach—and which technique closes the gap.
Three production engagements, 2024–2025 · Case studies ↓
Your team can't tell whether the answer is better retrieval, a LoRA adapter, DPO, full fine-tuning, or something else. So you pick the technique your most senior engineer knows best—or the one the last vendor pitched. You burn $10–50k and two months discovering it was the wrong one.
You've pushed prompting as far as it goes. You've built RAG and it's decent—but not good enough. The model still hallucinates on your domain terminology, misranks your edge cases, or drifts on long conversations.
Enterprise AI vendors want $500k and six months. General integrators can set it up but can't fix it when performance degrades. Hiring a senior ML engineer is $200–350k+ total comp, 3–6 months to first contribution—and they might still pick the wrong technique.
Fine-tuning vendors default to fine-tuning. RAG consultancies default to RAG. Prompt engineers default to more prompting.
None of them run the diagnostic first.
The right technique depends on your data characteristics, your quality requirements, your latency constraints, and your deployment environment. Picking the technique before analyzing the problem is backwards—but it's how the market works.
We start with your data, not our default.
We assess your data, your current pipeline, and your quality gaps across the entire adaptation stack: retrieval architecture, fine-tuning approaches (LoRA, QLoRA, full), instruction tuning, preference optimization (DPO/RLHF), and model compression. The output: a written recommendation on which technique your problem requires—with projected accuracy gains, compute cost estimates, and a build timeline.
We build what the diagnostic prescribes. Not what's familiar. Not what's fastest to bill. This could be a RAG architecture refactor, a LoRA fine-tune with custom evaluation sets, a full fine-tune with DPO alignment, or a multi-stage pipeline—whatever your domain data needs.
You get trained weights, training recipes, evaluation sets, deployment configs, and adapter templates. Your team maintains it without us. No vendor lock-in. No ongoing dependency. You own your model.
We're fluent across retrieval, fine-tuning, preference optimization, and compression. The recommendation comes from the diagnosis, not from our revenue model.
Every engagement ends with a handoff: trained adapters, eval sets, deployment configs, methodology docs. Your team runs it from there.
Every engagement builds on reusable adaptation templates—training configs, eval sets, and deployment patterns refined across prior engagements. You get faster time to production and fewer dead ends—because we've seen what works across domains, not just yours.
You have domain data and a production use case. Before committing $10–50k, you need to know which adaptation technique your data requires—not which one is most familiar to your team.
Your AI features work in demos but fall short on your domain. The model hallucinates on your terminology, misranks edge cases, or drifts on long conversations. You're past the easy fixes.
You've paid for an approach and performance isn't where it needs to be. You need a diagnosis: is the technique wrong for your data, is the implementation wrong, or both?
We don't take exploratory engagements. If you've shipped AI features that work in demos but fail in production, we can help. If you're still evaluating use cases, come back when you've hit the ceiling.
Book an Intro CallNot ready? Get the free technique selection guide →
We'll tell you within 30 minutes whether we can help. We work under NDA. Your data and architecture stay confidential.
An extraction pipeline had to survive three provider model changes in twelve months without regression or emergency retraining. Performance was degrading silently between swaps.
Failure analysis showed systematic gaps in domain terminology handling—not retrieval architecture. The bottleneck was in the model's weights, not the pipeline design. Fine-tuning was indicated; a RAG refactor was not.
LoRA adapter with a custom eval set covering domain-specific entities, edge cases, and adversarial inputs. Eval-gated deployment blocked any release that regressed below threshold.
An extraction pipeline passed every stakeholder demo but failed on roughly one in five real inputs. No clear definition of "correct" existed—quality was assessed by eyeballing samples.
The failure pattern wasn't prompt-engineerable. The model lacked domain schema knowledge that only training signal could provide. More prompting would not close this gap.
LoRA fine-tune with an eval set constructed from representative and adversarial inputs. Must-pass eval gates blocked releases that didn't meet the agreed acceptance criteria.
A team running full fine-tune cycles for each adaptation pass was burning weeks of calendar time and significant compute per iteration—a feedback loop too slow to act on.
LoRA adapters could match full fine-tune quality at a fraction of the compute for this task class. Full fine-tuning was overkill—not wrong in principle, but wrong for the specific data volume and quality requirements.
LoRA adapter pipeline validated against the same task evals used for the full fine-tune baseline—same task, same dataset, same quality threshold.
| Engagement type | Typical scope | Range |
|---|---|---|
| Standalone diagnostic | 2-week pipeline & data analysis; written technique recommendation | $5–8k |
| RAG / prompt architecture refactor | Lower complexity, shorter engagement | $25–30k |
| LoRA fine-tune + custom eval set | Most common starting point | $25–40k |
| Full fine-tune + DPO + benchmark suite | Complex domain adaptation | $40–60k |
| Ongoing retainer (3-month min.) | Adaptation as data drifts, new patterns, monitoring | $3–8k/mo |
Execution scope and pricing are defined in the diagnostic report. You'll know the exact cost before you commit to execution.
$5–8k · fixed scope · 2 weeks
In two weeks, we’ll analyze your pipeline, data, and failure modes across the full adaptation stack—retrieval, fine-tuning, preference optimization, compression—and deliver a written technique recommendation you own outright.
You can take the report to your internal team, another vendor, or engage Baseweight for the build. No obligation either way. Execution scope and pricing are defined in the report—so you’ll know the full cost before committing to a build.
Start with an Intro Call →We work under NDA. Your pipeline details stay confidential.
After two weeks, you’ll know exactly what to build—and what it will cost—before committing.
No. You need to have hit the limits of prompting and basic RAG. The whole point of the diagnostic is to determine the right technique before you spend money on the wrong one.
We'll tell you. If better prompting or a RAG refactor is the answer, we'll say so—even if that means a smaller engagement or no engagement at all. Recommending unnecessary fine-tuning would undermine everything we stand for.
It's a fair question. Our answer is structural: the diagnostic deliverable is a written report you own regardless of what you do next. If we recommend fine-tuning and you want to take that report to an in-house team or another vendor, that's fine. We've told clients their stack is fine and the engagement ended there—those clients refer others precisely because we didn't oversell.
The diagnostic takes 1–2 weeks. Execution varies by technique: a RAG refactor might be 3–4 weeks, a LoRA fine-tune 4–6 weeks, a full fine-tune with DPO 6–10 weeks. We scope this precisely during the diagnostic.
We work under NDA and can operate within your infrastructure. For fine-tuning, we need access to representative training data. We agree on data handling requirements before any access.
Everything. Trained weights, adapters, evaluation sets, training recipes, deployment configs. No ongoing licensing. No vendor lock-in.
A senior ML engineer is $200–350k+ total comp, takes 3–6 months to onboard, and may default to the technique they know rather than the one your problem needs. We bring experience across multiple domains and problem types, and deliver in weeks, not quarters.
That's fine. The diagnostic is most valuable before you've committed to a technique. We define which approach your data needs so you build it right the first time—instead of retrofitting after a failed attempt.
Both. The diagnostic produces a recommendation. If you engage us for execution, we build it—code, training pipelines, eval sets, deployed weights. We hand you working artifacts, not a slide deck.
Two free tools to help you assess your adaptation readiness before a call.
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If your AI features plateau at "good enough for a demo" but fall short in production, the technique—not the team—is usually the bottleneck. Book 30 minutes and we'll tell you which adaptation approach your data needs.
We take one technical engagement at a time. If you're past the prompting phase, it's worth a conversation now.
No pitch deck. No sales sequence. Just a technical conversation about your stack and where the quality gap is.