Supporting service · Engineering
Research-to-Production
Your systems improve as the field advances. A managed path from new research to your production systems. Every technique is screened, reproduced, and tested on your work, and a person approves it before it ships.
Is this for you?
- Businesses with AI in production that should keep pace with the field
- Teams without time to read, test, and vet new techniques themselves
- Owners who want improvements adopted on evidence, with rollback kept
The situation
The field keeps moving. Your system should move on evidence.
Your AI system is in production and doing useful work. Meanwhile new models, methods, and tools appear often, each with a claim attached. Someone on your team is expected to know which ones matter.
Reading a paper is the easy part. Rebuilding the technique, testing it on your own work, and checking its license and security take sustained engineering time. That work competes with everything else your engineers own.
What you want is a steady answer to one question. Would anything new improve our system, measured on our own work, and is it safe to adopt?
Our approach
A standing pipeline, anchored to your accepted baseline.
We keep a record of every candidate we consider for your workloads: its source, its license, and our screening decision. Only candidates relevant to your work, with terms you can use, go further.
A candidate that passes screening is rebuilt and measured against your accepted baseline, on the same held-out test set. We compare quality, reliability, latency, review effort, and cost per accepted outcome. Research agents can help search and summarize, and an engineer reviews every finding.
Nothing reaches your systems without a person’s approval. Your named approver sees the evidence, the risks, and the release plan. Declining to adopt is a valid result, and the evaluation is kept for the next review.
Use cases by industry
Where this service fits.
Typical applications across industries. They show where the service applies, not past client work or results.
- Credit union
Evaluating a new document extraction model
A member services workflow extracts details from loan documents, and staff confirm each record before it reaches the core system. When a new extraction model is released, we screen its license, reproduce its claims, and measure field accuracy on the held-out set. The lending manager approves any release, and the current version stays ready for rollback.
- Engineering consultancy
Testing a retrieval method on design standards
Engineers use an AI assistant that answers questions from design standards and past project reports, and an engineer checks each answer used. A new retrieval method is rebuilt and measured on held-out questions, with the same scoring rules used at acceptance. The principal reviews the evidence and the release plan, then decides whether to adopt, defer, or decline it.
- Retailer
Checking a smaller model for product questions
A customer question workflow drafts answers from product data and store policies, and staff send or edit each reply. When a smaller open model appears under usable license terms, we measure quality, latency, and cost per accepted answer against the baseline. The digital lead reviews the evidence, the risks, and the release plan before approving any change.
- Hotel group
A multilingual model for guest messages
Front desk teams answer guest messages from AI drafts, and a staff member sends every reply. A new multilingual model is screened for license and data terms, then measured on held-out guest messages in the languages the hotels receive. The guest services director approves any release, with monitoring signals and a rollback route agreed in advance.
- Restaurant group
A published forecasting technique for prep planning
A forecasting workflow suggests daily prep quantities from sales history and reservations, and each kitchen manager confirms or adjusts the plan. A published forecasting technique is reproduced on neutral data first, then compared with the accepted baseline across held-out weeks of the group’s own sales. The operations director sees accuracy, review effort, and running cost side by side before deciding.
- Testing laboratory
A new method for report narrative drafting
An AI workflow drafts test report narratives from instrument results, and a qualified analyst signs each report. A new long-context method is rebuilt and checked for license terms, new dependencies, and data flows before it touches lab records. The quality manager approves a release only when measured accuracy holds against the baseline, and rollback stays ready.
- Translation services
Screening new models for terminology accuracy
Translators post-edit AI drafts of client documents, and a lead linguist approves each delivery to the client. New models are screened for license terms and data handling before any client text is used, then measured on terminology accuracy and post-editing effort. Declining a candidate is a valid result, and the evaluation is kept for the next review.
- Media publisher
Evaluating new tools for archive tagging
An AI workflow tags archive articles and photos for search and reuse, and editors approve tags before they are published. New tagging models and tools are screened for license terms, reproduced on neutral examples, and measured on a held-out sample of the archive. The managing editor approves any change, and the previous version stays available for rollback.
What you receive
Screened intake
New papers, models, and methods reviewed for relevance to your workloads, with licenses checked.
Reproduction and benchmarks
Promising techniques rebuilt and measured on representative, held-out examples of your work.
Security and licensing review
New dependencies, data flows, and terms checked before anything reaches your systems.
Controlled adoption
Approved changes released with monitoring in place and a rollback route preserved.
How it works
- Intake and screeningRead the primary source, its license, and the assumptions it makes about data and scale.
- ReproduceRebuild the technique and confirm it behaves as described on neutral examples.
- Benchmark on your workCompare it with your accepted baseline, on your held-out test set.
- ApproveYour named approver reviews the evidence, the risks, and the release plan.
- Release and monitorShip the approved change, watch the agreed signals, and keep the rollback ready.
How success is measured
The measures your approver signs.
Each measure goes into the acceptance criteria with its test data, threshold, and the person who checks it.
- Improvement over the accepted baseline
- Each candidate is scored on the same held-out test set and rules used at acceptance. An adoption recommendation needs a measured improvement.
- Reproduction fidelity
- Whether the rebuilt technique behaves as its source describes on neutral examples. Differences are recorded before any client data is involved.
- Review effort per item
- The time and edits reviewers need for each output, under the candidate and under the baseline. It is checked from reviewer records during evaluation.
- Cost per accepted outcome
- Running cost divided by the outcomes reviewers accept, compared across candidate and baseline. It shows whether a gain in quality carries a cost.
- Release readiness
- Security and licensing review complete, monitoring signals agreed, approval recorded, and rollback route tested. Each item is checked before release.
Where care is needed
What we watch, and how it is handled.
- Published claims and your work
- Published results come from other data and conditions. We reproduce each technique, then measure it on your held-out work before any recommendation.
- Licenses and terms
- Model and code licenses can limit commercial use or change over time. We record each candidate’s license at screening and recheck it before release.
- New dependencies and data flows
- A new technique can bring new libraries, services, or places your data travels. Security review covers each one before anything reaches your systems.
- Test set integrity
- A held-out set keeps its value only while candidates are not tuned against it. We keep it separate from development work, and your team keeps ownership of it.
- Research agents
- Research agents help search and summarize sources. An engineer reviews every finding, and only your named approver can release a change.
Who does what
Your team decides. We engineer.
Your team
- Name the person who approves each release
- Agree which workloads are in scope and their priority
- Keep ownership of the accepted baseline and held-out test set
- Provide access to monitoring signals for released changes
- Approve, defer, or decline each candidate on the evidence
Sophrono
- Screen new research, models, and tools for relevance and license
- Reproduce promising techniques and measure them on your work
- Review security, dependencies, data flows, and terms before release
- Prepare each release plan with monitoring and a rollback route
- Report what was screened, adopted, deferred, and declined
At the end
The decisions you make next.
The service ends with evidence and a choice. Each option is yours, and none is assumed.
Adopt the change
Your approver releases it with monitoring and rollback in place. The release is then watched against the agreed signals.
Defer and revisit
File the evaluation and revisit it when the technique matures or your work changes. The record shows what was tested and why.
Decline
Nothing changes in your systems. The current setup stays in place, and the screening record explains the decision.
Build on it
Where an adopted change opens further gains, Iteration Sprints deliver them against agreed criteria. AI Stewardship can keep the whole system measured and current.
Before we start
What to have ready.
- An AI system in production, with the workloads you want kept current
- An accepted baseline and a held-out test set, or a plan to create them
- A named person who can approve releases for each workload
- Your constraints on data handling, hosting, and licensing
Related services
What “accepted” means
Measured against criteria you agree to in advance.
- Each candidate is recorded with its source, license, and screening decision.
- Adopted changes show a measured improvement over the accepted baseline.
- Security and licensing review is complete before release.
- A named person approves every release, and rollback is preserved.
Full engagement terms are finalized in a Master Services Agreement.
Ask about a technique
Tell us what you want to test.
We reply with how we would screen, reproduce, and measure it on your work.
- A senior engineer reads every request
- A reply by email with the next step
- No obligation until scope and price are agreed
Not ready to scope this? Ask an engineer first: a free 15-minute call that names the agentic systems that could fit.
The Canon
The rule this service delivers.
Supports
The core services it supports.
Supporting services are often scoped inside a core engagement, or run on their own.
Before and after
Not ready yet? Super Intelligence Newsletter. After this: AI Stewardship or Agent Assurance.
Questions
Does new research go straight into our systems?
Never. Every technique is reproduced, benchmarked on your work, reviewed, and approved by a person before release.
How is this different from AI Stewardship?
Stewardship keeps a live system measured and current. Research-to-Production is the dedicated pipeline for evaluating what is new, and it can run inside a stewardship plan.
What happens if an adopted change underperforms?
The rollback route is kept for every release, and the monitoring signals are agreed in advance.
Which sources do you screen?
Technical papers, model releases and model cards, open-source repositories, and license changes, among others. Each candidate is read at its primary source and recorded with our screening decision.
Do we need a test set before we start?
Adoption decisions need one, because every candidate is measured against your accepted baseline. If you do not have one yet, creating it is the first piece of work, and your team keeps it.
What if nothing is worth adopting?
Then nothing changes in your systems, and you receive the record of what was screened and why. Knowing the current setup still leads is a useful result.
Can our team suggest a technique to evaluate?
Yes. Suggestions enter the same pipeline as any other candidate: screening, reproduction, measurement on your work, review, and a person’s approval.
How is the work priced and agreed?
Scope and price are set in a proposal, based on the workloads in scope. Full engagement terms are finalized in a Master Services Agreement.
Do you consider open and closed models?
Yes. Candidates include open and closed models, as well as methods and tools. Each is screened for license and terms, and measured on your work in the same way.
Who approves a release?
A named person on your side for each workload. They review the evidence, the risks, and the release plan, and nothing ships without their approval.
Do research agents change our systems?
No. Research agents help search and summarize sources, and an engineer reviews every finding. Only a release your approver signs off changes your systems.
Is our data used to reproduce techniques?
Reproduction uses neutral examples first. Your held-out work is used for measurement, under the data handling constraints you set.
What do we receive between releases?
A report of what was screened, adopted, deferred, and declined, with the reason for each decision. Your team keeps a record of the field as it relates to your work.
Can this cover a system Sophrono did not build?
Yes, where an accepted baseline and a held-out test set exist or can be created. Every candidate is then measured against that baseline.
Which workloads come first?
Your team sets the priority of the workloads in scope. Screening favors candidates relevant to those workloads, under terms you can use.
What if a candidate needs new hosting or hardware?
The evaluation states the hosting the candidate needs and its running cost. Any hardware is itemized separately, and your business buys and owns it.
When are security and licensing checked?
License and terms are checked at screening, before any client data is involved. Dependencies, data flows, and terms are reviewed again before release.
Does this suit a team with its own engineers?
Yes. Your engineers can suggest candidates and review evaluations, while we carry the screening, reproduction, and measurement work. Release approval stays with your named approver.
How is a released change monitored?
The monitoring signals are agreed in the release plan before approval. If they move, the rollback route returns the system to the previous version.