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Research report
AI Does Not Remove the Bottleneck
Find your real workflow constraints before buying AI tools.
AurvikAI · LLM Fine-tuning
Fine-tuning teaches AI how to behave. Retrieval tells it what is true.
We help you choose the simplest and most cost-effective way to improve AI, then fine-tune only when it is genuinely the right answer.
Most fine-tuning requests can be solved faster and more affordably with better instructions or access to the right information. When fine-tuning is necessary, we prepare high-quality training examples, choose the right method, and prove the improvement using real business tasks.
600+
Software products and digital solutions delivered
70+
Countries using software built by our team
15+ years
Of combined team experience
Clients we've worked with
Four situations where fine-tuning can create real value.
Fine-tuning works best when AI needs to behave differently, not simply learn new facts.
We define success before changing the model.
Training without a proper test is expensive guesswork. We first collect real examples of the task, agree on what a good answer looks like, and measure the current model. This gives us a clear starting point. If fine-tuning does not improve the result enough to justify its cost and maintenance, we tell you. That has happened.
Training scores are not business results. We measure performance on the task that matters to you.
Discuss your fine-tuning project18+ years
Of SaaS and AI leadership behind every engagement
250+
Products built using our founder's frameworks
Fine-tuning from preparation to production
High-quality training data, the right tuning method, clear proof of improvement, and a plan for future updates.
Match the method to the task, budget, and required level of change.
Efficient fine-tuningCore
Adapt model behaviour without retraining the entire system, which suits most business needs.
Full fine-tuningAdvanced
Make deeper changes only when the task and available data genuinely require them.
Instruction tuningBehaviour
Teach the model to follow a consistent response pattern across specific tasks.
Preference tuningAlignment
Shape responses around the choices and quality standards of your expert reviewers.
Three ways to improve AI. Most businesses need the first two.
Prompting is the fastest and cheapest place to start. Retrieval supplies missing or changing knowledge. Fine-tuning changes behaviour and adds ongoing maintenance. We test them in that order and stop as soon as one solves the problem properly.
Prompting
- Gives the model clearer instructions and examples.
- Use it when the model can already do the task but needs better direction.
Retrieval
- Gives the model access to your current documents and information.
- Use it when the model does not know your policies, products, or latest facts.
Fine-tuning
- Changes the model's behaviour, structure, tone, or specialised reasoning.
- Use it when instructions and retrieval cannot deliver consistent results.
A fine-tuned model needs a long-term plan.
General AI models improve quickly. A future standard model may eventually match or beat today's tuned version. That is good news, but your team needs a reliable way to know when it happens. We create a reusable testing process and training setup so you can compare new models, update efficiently, or stop fine-tuning when it is no longer needed.
The evaluation set and training process belong to you. So does the decision to stop paying for tuning when a standard model catches up.
30
Specialists across engineering, AI, design, and delivery
Every engagement
Hands over the evaluation set, training process, and approved data
Questions businesses ask about fine-tuning
Clear answers about data, model choice, results, privacy, and ongoing cost.
How much training data do we need?
Usually fewer examples than people expect, but they must be high quality and consistently reviewed. A few hundred strong examples can outperform thousands of weak ones. We assess what you already have before recommending further investment.
Can we use customer data?
Only when your agreements, privacy rules, and regulations allow it. We answer that question before training begins. If the data cannot be used, generated examples or secure retrieval may offer a safer route.
Which model should we fine-tune?
The right model depends on where it must run, what it may cost, how private the data is, and what its licence permits. Once those requirements are clear, the choice becomes much easier.
How do we know fine-tuning worked?
We compare the tuned model with the original model using the test created before training. If the improvement is not large enough to justify the cost, we say so. That is a useful finding, not a failed engagement.
What does it cost to maintain?
Ongoing costs may include retraining, storage, hosting, and testing new base models. Many proposals leave these costs out. We explain them before the project begins.
Practical guidance for choosing the right AI approach
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Selected work
AI systems running in production, described rather than named.
An industrial hygiene consultancyTurning lab data into review-ready reportsLab PDFs read automatically, exposure limits checked against the right jurisdiction, and report prose generated with the regulation cited.Under 1 hrto produce a report that used to take six to tenRead the case
A creator-tools startupValidating the product before funding the buildA rapid concept workshop that checked every platform assumption against the real API docs — and corrected several of them.10 daysfrom first workshop to a fundable, build-ready packageRead the case
A Los Angeles private car serviceTurning every call into a booking.Phone and web bookings answered by AI, written straight into the dispatch system, then rebuilt as a product other operators can buy.24/7booking cover on phone and chat, with nobody on the lineRead the caseWhat we build with
Models
- Anthropic Claude
- GPT (OpenAI)
- OpenAI Embeddings
Evaluation
- Evaluation harnesses
- Regression suites
Serving
- FastAPI (Python)
- AWS
- Docker
Partners in delivery
AI Cloud PartnerPartner Network
Not sure whether you need fine-tuning?
Most businesses asking for fine-tuning actually need better instructions or retrieval. Bring us the task, a sample of your data, and the result you need. We will explain which approach fits best and what each option will cost.