What our SLM development services include
Aranv offers small language model development for one repeated job: a small language model (SLM) built from open-weight base weights and your examples to classify requests, extract fields or draft one document type, sized for one GPU, a CPU server or a device once tests confirm it fits.
Among the options under train your own AI model, the narrow one fits a single job. Try prompts and retrieval first, because a small model is a poor home for facts that change: retrieval supplies those, and training supplies behavior. If the small model cannot outscore that baseline on your own test cases, we stay with the simpler setup and tell you so.
We do not normally train from zero. The work begins with open-weight base weights, and their license comes first, because its terms still apply to what we build on them. Families and sizes change quickly, so we check our AI model comparison and each maker’s own pages at the start. Google’s Gemma documentation warns that fine-tuning needs far more memory than inference.
One job, one compact model
From a sample job to a small model
- 01 Define the one job Inputs, outputs and any cases reserved for a person are written down.
- 02 Pick a base that fits Candidates are compared by size, license and target hardware, using makers’ current pages.
- 03 Tune, then compress Training on your reviewed examples comes first, quantization afterward, and each step is retested.
- 04 Route the rest Routine requests stay on the small model. Harder ones pass to a larger model or staff.
Smaller is not automatically better or cheaper. Measure error rate, response time and hardware needs on your own test set.
Where a custom SLM can fit
These jobs have a fixed shape: the input looks similar each time, and someone can check whether the output is right.
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Inbound request sorting
Each incoming email, form or work order gets a label from the queues your staff already use, with uncertain ones held for a person.
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Field pull from one form
One document type, such as a delivery note or inspection sheet, comes back as the fields your system imports, flagged when the model is unsure.
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One-document drafting
A single repeated document, such as a service visit summary, drafted in the layout your team approved and ready for someone to review and send.
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On-device and offline work
A rugged tablet or site server where connectivity is weak, after testing that the chosen model fits that machine’s memory and speed.
Compression and distillation
Quantization stores weights at lower precision, which shrinks memory use and may change answers, so we retest after it. Distillation trains a small model on a larger one’s outputs. Some providers’ terms limit using their outputs to train other models, so we check those terms before that step.
What we do
- Quantization trials at several precision levels
- Accuracy check after every compression
- Distillation only after a terms check
- Memory and speed on target hardware
Routing: small first, larger when needed
A router sends routine requests to the small model and passes unusual or low-confidence ones to a large language model (LLM) or a person. We log every handoff, so you can see what the small model should learn next, or where it should stop trying.
What we do
- Confidence thresholds set from test results
- Fallback to a larger model
- Handoff to staff for exceptions
- Logged reason for each handoff
- Report on requests handled locally
How we approach a project
Where the model runs decides much of the design. Edge AI development covers phones, tablets and site devices, and private LLM deployment covers a server in your own environment. For background, read SLM vs LLM and our small language model explainer.
Some work does not belong on a small model. Open-ended reasoning, broad research and questions that range across many subjects need a larger model or a different design, and we will tell you so early. To test your job, book a 30-minute conversation.
How we work
- One job for each small model
- Retest after every compression step
- Hard requests get routed elsewhere
How we build a small language model
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Capture the job on paper
Your team writes what comes in, what should come out and what counts as an error, using real samples.
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Shortlist bases for your hardware
Base models are compared on your device or server class, with licenses read before any tuning starts.
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Tune on approved examples
Subject experts approve the examples, and each tuning run is scored on cases it never trained on.
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Compress, route and release
We shrink the winner, retest it, set the routing rules and begin with a limited share of requests.
Why Aranv for small models
Measured, not assumed
We do not assume a small model saves money or matches a larger one. Error rate, speed and running cost are measured on your job before any recommendation.
Plain about the limits
When your job needs open-ended reasoning or broad research, we recommend a larger model or a different approach.
Tested on your machine
Published figures describe someone else’s setup. We test candidates on the device or server class you will really use.
Handoffs your staff can see
When the model passes a request along, the reason is logged and visible to your team, so nobody guesses why.
Planning Small Language Model Development?
Book a 30-minute conversation with the team that would build it.
Small Language Model Development FAQs
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What kinds of work suit a small model?
Repeated work with one clear output: classifying requests, pulling fields from one form type or drafting a single document. It is a poor fit for open-ended reasoning or broad research.
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Is a small model cheaper or as accurate as a larger one?
That depends on the job, so we measure it and do not promise it. A smaller model may need less hardware, and accuracy on your task may hold or drop. We compare both on your test set and count the requests passed along to a larger model.
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How is this different from fine-tuning an LLM?
Fine-tuning alters a single behavior of an existing LLM. Small model work also deals with size: choosing a base for your hardware, compressing it, retesting and routing what it cannot handle. Tuning is one step inside that work.
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Is a domain-specific SLM enough for our work?
It can be, if the work is narrow and your examples cover its usual cases. It is not enough when staff ask open questions across many subjects.
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Can a small language model run without a GPU?
Some can, for some workloads. It depends on model size, compression, input length and the response time you need. We test on the actual machine.
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Who owns the small model you build for us?
Ownership is agreed in the contract before the work starts. Our recommendation is an agreement under which the training data, test sets, code, adapters and weights pass to you when the work has been paid for. A license on the base model still applies. Your data is used for your model.
Let’s talk about your next step
Contact Aranv to discuss your workflow, current tools, and goals. Meet with our team or send an inquiry.
Aranv at a glance
- 9Years in business
- 120+Clients served
- 300+Projects delivered
- 200+Team members
What happens next
- Pick a time, or send your project details.
- We review your goals, current tools and timeline.
- You get a clear next step: a scope, plan or estimate.
Book a meeting with our team
Choose an available date and time for a 30-minute conversation about your project.
- 30 minutes
- Online video call
- Times in your time zone
Tell us about your project
Share a few details so we can discuss a useful next step.
Prefer email? sales@aranv.com