Drafts that need less rewriting
Use approved examples to help AI follow your tone, terms and standards. Give your team a more useful starting point for replies and other written work.
Model Fine Tuning & Optimization
Less correcting.
More useful AI.
Your team keeps rewriting AI drafts, fixing the same mistakes or waiting on simple tasks. We find the cause and improve the way AI does the work, without giving you a technical project to manage.
You show us the work. Your squad handles the technical decisions.
Your work sets the standard
Customer
replies
replies
Forms &
records
records
Business
questions
questions
Your standard, not a sales demo
What good
looks like.
- Uses the terms your team uses
- Follows the format you need
- Includes the details that matter
Compare the simplest ways to improve
Clearer
instructionsBetter
informationA better
model fit
instructionsBetter
informationA better
model fit
- Your real examples
- Changes tested before rollout
- A squad that handles it
Businesses that trust DevSquad
Find the right fix
Start with the problem, not the model.
The same bad answer can have different causes. We check the simple fixes first and recommend tuning only when the work calls for it.
-
Missing business information?
Connect the right records first. An answer about a customer, order or current policy needs information from your business, not a guess.
-
Good information, unclear instructions?
Make the task clearer. Show useful examples and explain what an acceptable answer includes. That may solve the problem without training anything.
-
The same mistakes keep coming back?
Compare other models or train for the patterns that matter. Keep a change only after testing it on work it has not already seen.
Our expertise
What gets easier for your business
The goal is useful work with less effort from your team. We agree on what better looks like, then test the changes against it.
More consistent routine work
Reduce repeated mistakes when AI sorts requests, reads documents or prepares records. Test the usual cases and the awkward ones your team sees.
Answers in the format you need
Get the fields, summaries or layouts your team actually uses. Spend less time reshaping an answer before it can move into the next step.
Less waiting on simple tasks
Find what slows down a reply, lookup or routine request. Compare ways to get the work moving while keeping the quality your team needs.
Less waste on routine AI work
Not every job needs the biggest model. We check whether a simpler option can handle the routine work without losing the quality that matters.
Changes your team can judge
Compare the old and new results on the same work. See whether the answers are more useful, need fewer corrections and fit the way your business runs.
Technical services
What we build behind the scenes
The technical options your squad can use. We choose the work your business needs, not a bigger project by default.
-
Model Fine Tuning
Train a model on approved examples to improve a specific behavior, format or use of business terminology.
-
Model Distillation
Use a larger model to help train a smaller specialist, then compare quality and running requirements for the task.
-
Model Portfolio Optimization
Evaluate model choices and route each task to an option that meets its quality, speed and resource requirements.
-
Evaluation & Benchmarking
Compare changes with your current setup using representative cases kept separate from the training examples.
-
Private & Self Hosted Deployment
Assess hosting options around data access, model licensing and the systems you already use.
-
Inference Optimization
Improve response time and resource use through model choice, caching and more focused requests.
Your business first
Better AI. Without another team to manage.
Your AI & Data Modernization Squad handles the review, testing and improvements. If the software around your AI needs work too, our Product Development Squad can build or update it.
-
You bring the business knowledge
Show us examples of work you accept and work you have to fix. Your product manager coordinates the team, with about one to two hours of your time a week for questions and review.
-
We test before changing the work
Compare results before rollout, introduce changes in manageable steps and keep a way back if a new approach does not hold up.
-
Your information stays under your control
You keep your data, custom code and accounts. We agree on who can access your information and where it can be used before anything changes.
A clear path forward
Compare the results. Then make the change.
Agree on what good work looks like, try the simplest improvements and build on what proves useful.
-
01
Understand the problem
Discovery Workshop
Show us where AI falls short and how your team corrects it. We review the tools, information and examples before recommending what to change.
-
02
Try the options
Sprint Zero
Get a clear plan and compare possible improvements on sample work. If the change needs new screens, review a clickable prototype before the build.
-
03
Put the improvement to work
Build with your squad
Your squad makes the chosen changes, checks them on fresh examples and helps your team use the results. We keep reviewing the work as your business changes.
Before we get started
Questions, answered.
What to know before changing the AI your business relies on.
Have another question?What is model fine tuning?
It is additional training that helps an existing AI model follow patterns in approved examples. That might mean using your terminology, following a format or handling a recurring type of task more consistently. It is one possible improvement, not the starting point for every project.
Fine tuning or RAG: which one do we need?
RAG gives AI access to relevant information when it answers. Fine tuning changes patterns in how a model responds. If AI needs current customer records or policies, connecting that information is usually the first step. If it repeatedly gets the style, format or a specialized task wrong, tuning may help. Your squad tests what the problem actually needs.
How much data do we need to tune a model?
It depends on the task and the model. Useful, representative examples matter more than a large pile of unreviewed records. We help select and prepare the examples, check that you have permission to use them, and keep separate cases for testing the result.
Do we need special hardware or a technical team?
You do not need to manage developers or buy equipment before we start. Your squad handles the technical decisions and explains the setup that fits the work. That may use an existing service or, where appropriate, a model running in an environment you control.
Will we have to start over when a new model comes out?
We keep your examples and tests separate from any one model so we can compare new options against the work you care about. A newer model is not automatically a better choice. We check the results and the effort involved before recommending a switch.
How do you measure whether the changes worked?
We agree on what a useful result looks like and compare it with your current setup. We look at mistakes, corrections your team still has to make, response time and the resources used for the task. Testing includes examples that were not used to train the model.
Can tuning make AI right every time?
No. Tuning can improve repeatable patterns, but it does not guarantee a correct answer or replace access to current business information. We test the changes, make uncertainty visible and keep people approving decisions that matter.
What if we are not using AI yet?
Start with the work you want help with. You may first need to connect your business systems or build an agent around a recurring task. A standard model may be enough. We help you choose that path before considering training or other extra work.
How soon can we see whether a change helps?
We begin with a focused task and agree on the first comparison during discovery. The condition of your examples, access to your existing tools and the changes being tested shape the schedule. You see the evidence before deciding how far to take the work.
Who owns the work and the model?
Your business data and the custom code we create for you remain yours. Rights to a model depend on its license and the service used to run or train it. We explain those terms before choosing the setup, so you know what you own and what depends on a provider.
Show us what your AI keeps getting wrong.
Bring the drafts, documents or repeated tasks your team has to fix. We’ll help you find the simplest improvement worth making.