Current state
- You explain away bad outputs instead of measuring them.
- You discuss model quality without knowing the failure budget.
- You estimate token cost from guesses.
- You delay launch because no one wants to sign off.
Your AI feature is stuck because nobody trusts it yet.
The demo works. The production decision does not. Latency is hard to defend, token cost keeps moving, and failure cases still reach the customer. I find the bottleneck, prove the fix, and turn the feature into something your team can ship.
Founder-led. Built for teams that need production confidence before they hire a full AI function.
The bottleneck
It is not an AI problem. It is a trust gap in the product.
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The symptom
The prototype performs in controlled demos, then slows down, drifts, or produces answers your team cannot confidently put in front of customers.
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The cause
There is no production system around the model: no reliable evals, no cost controls, no retrieval boundaries, no security posture, and no clear owner for the user experience.
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The consequence
The roadmap stalls. Sales loses the story. Product keeps re-scoping. Engineering carries risk without a decision framework.
That is the hidden bottleneck: production confidence. Until it is solved, your AI feature stays technically impressive and commercially fragile.
From stuck to momentum
The work is to move one feature from fragile promise to shippable system.
Desired state
- You know which failures matter and how often they happen.
- You have guardrails around retrieval, prompts, permissions, and UX.
- You can defend latency and cost with real numbers.
- You make one clear shipping decision per cycle.
The method
Stabilize, prove, ship.
That is the operating method. Stabilize the architecture. Prove the risks with evals and numbers. Ship the smallest reliable version your customers can use. Every cycle ends with one concrete decision: kill it, fix it, or move it forward.
Offer ladder
Start low-risk. Increase access only after value is clear.
AI Ship Readiness Diagnostic
A fast check for the five guardrails missing from most AI features before production.
You see the bottleneck before a sales call.
Production Readiness Sprint
A short, focused intensive for one qualified feature with visible production risk.
You leave with a straight verdict and the next action.
AI Shipping Room
A longer working program for teams that need a repeatable cadence for evals, guardrails, cost control, and launch decisions.
Your team builds the muscle to ship one reliable improvement per cycle.
Embedded AI Product Lead
Close access for teams that need architecture, UX, implementation leadership, and decision support around a business-critical AI feature.
You get speed, senior judgment, and direct ownership when the cost of delay is high.
Concrete process
No abstract consulting. One feature. One decision loop.
The work starts with evidence, not opinions. We isolate the feature, measure where trust breaks, and turn the findings into an implementation path your team can actually use.
- A production-confidence verdict on the feature as it exists today.
- Measured latency, cost, retrieval, hallucination, and security failure points.
- A guardrail map across prompts, data, permissions, evals, and UX.
- A scoped implementation path with the next decision clearly named.
If the right answer is do not ship this version, that is the answer. Avoiding a bad quarter is progress.
Delivery cadence
Four moves, repeated until the feature can carry business risk.
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Diagnose
Find the bottleneck behind the visible symptom and decide whether the feature belongs in the sprint.
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Instrument
Add the evals, traces, cost views, and failure categories needed to stop debating from anecdotes.
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Stabilize
Tighten architecture, retrieval, prompts, permissions, and interface behavior around the risk that matters most.
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Decide
End the cycle with one action: ship, repair, narrow the scope, or stop.
Free diagnostic
Run the 2-minute AI Ship Readiness Diagnostic.
Five checks. One feature. You will know whether your bottleneck is evals, retrieval, latency, security, ownership, or a mix your team needs to resolve before launch.
Proof context
Relevant systems built and delivered under NDA. No invented names. No inflated claims. Just the kind of production work this page is selling.
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Industrial ML at about 5,000 samples per day
Anomaly detection, classification, and auto-tagging for abnormal results across viscosity, metals, and contamination. The outcome was not a demo. It ran against daily production load.
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RAG over company-owned knowledge
Contextual answers with source attribution and semantic search. On a comparable build, knowledge ingestion moved from ten minutes to one.
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AI inside an HR platform already in use
Employee questions answered and workflows automated inside the tools the team already used. No migration. No new platform adoption burden.
Fit
This is for teams that need a shipping decision, not AI theater.
For you
- You have an AI feature, prototype, or workflow already in motion.
- The business value is clear, but production risk is blocking momentum.
- You can share real failure cases, constraints, and usage context.
- You want direct judgment and visible next actions.
Not for you
- You want a generic AI strategy deck.
- You need someone to invent urgency or proof for a vague idea.
- You are not ready to expose the technical and business constraints.
- You want several unrelated AI experiments running at once.
FAQ
Objections, answered directly.
Do we need a finished product?
No. You need a real feature, real users or expected usage, and a business reason to make a production decision.
Is this only for RAG?
No. RAG is common, but the bottleneck can also be evals, agent permissions, cost, latency, data boundaries, or UX failure.
Why start with a diagnostic or sprint?
Because price and scope are meaningless until the production risk is visible. The first move is to prove what has to change.
What if the feature should not ship?
Then you get that answer quickly. Stopping a fragile launch is a business outcome.
Contact
Bring the feature that is stuck.
Twenty minutes. Bring the symptom, the constraint, and the launch pressure. You will leave knowing whether this is a fit and what the next decision should be.
Or email directly: hello@dewitt.studio