Use-case & data plan
Decision, data, success metric.
Product · Models
AI model development and integration — built for your data, not a generic demo.
A model is only as good as the data and the evaluation around it. Most 'custom AI' fails because neither was real.
We build and integrate models against your actual data, with guardrails and regression tests so performance does not quietly decay.
We do not invent client logos. Capability demos are labeled until named cases are cleared.
In practice
How this actually shows up in a real engagement.
We scope the job before the model: what decision it supports and how we will know it is right.
Data preparation is treated as product work — cleaning, labeling, and split discipline matter more than the architecture.
Fine-tuning and RAG are chosen on evidence, not hype; retrieval quality is tested before the model is trusted.
Evaluation sets with golden cases catch regressions when prompts, data, or models change.
Integration is production-grade: latency, fallbacks, and monitoring, not a notebook that 'works on my machine.'
Who it's for
Operators who need a system — not a one-off deliverable.
- Teams with proprietary data worth a custom model
- Products needing retrieval over internal knowledge
- Builders who need a model wired into real systems
Honestly not the best fit if…
- You want a model with no data and no evaluation
- You expect magic from a one-line prompt brief
- You refuse any monitoring after launch
What you get
Deliverables that connect to the rest of GTM.
Each line item is designed to hand off cleanly into creative, demand, brand, or product — not sit in a silo.
Model build or tune
Fine-tune, RAG, or both.
Evaluation harness
Golden cases, regression checks.
Integration & docs
Production wiring, runbook.
Outcomes we optimize for
Numbers and behaviors — not vanity theater.
- Models that reflect your data and domain
- Fewer wrong answers in production
- A defensible evaluation you can trust
- Clean integration into existing systems
How we work
A clear loop — diagnose, ship, measure, compound.
01
Frame
Job, data, metric.
02
Build
Train, tune, retrieve.
03
Evaluate
Golden tasks, edge cases.
04
Integrate
Ship with monitoring.
Common failure modes
What we see break — and how we refuse to repeat it.
No evaluation
Silent quality decay.
Dirty data in
Confident wrong answers.
Demo, not production
Falls over on real load.
Investment
How this is usually scoped.
Discovery + build fixed fee, then monthly for monitoring and iteration. See /services#investment.
Full ranges live on the services investment section. Quotes follow diagnosis — not a menu price list.
AI Model Development & Integration FAQ
Straight answers
No theater — just how this service actually runs inside an AI-powered GTM studio.
Fine-tune or RAG?
Whichever the evidence supports. Often RAG first, fine-tune when retrieval alone is not enough.
Which models and platforms?
Job-based. We avoid lock-in theater and pick for reliability, cost, and your data needs.
How do you protect our data?
Minimization, access control, and clear retention — no casual dumps into public models.
Will it stay accurate over time?
Evaluation harness and monitoring catch drift; we retrain or adjust before users notice.
Keep reading
Related pages and playbooks
Ready to run this as part of one GTM system?
Tell us the outcome. We'll name whether this lane is first — or if something else is leaking harder.
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