Uboreshaji wa modeli
Uboreshaji wa modeli
Programu nyingi za AI zinalipa kwa usahihi ambao hazina haja nao.
- Sekta
- Huduma
- Unachopata
- 7
Mbinu yetu
Uboreshaji wa modeli
A model that is accurate enough at a tenth of the cost is usually a better business decision than a better model at ten times the cost. We measure what your task actually needs, then close the gap between that and what you are running.
Work ranges from fine tuning on your own labelled data and distilling a large model into a small one, through quantisation so it fits the hardware you already own, to prompt and retrieval improvements that lift accuracy without touching the weights at all.
Everything is measured against an evaluation set drawn from your real cases, so a change is only kept if it holds up on your data rather than on a public benchmark.
Unachopata
Kila mradi huzalisha nyaraka unazomiliki na unaweza kumpa mhandisi au mkaguzi yeyote.
- Evaluation set built from your real cases
- Accuracy, latency and cost baseline for the current setup
- Fine tuning on your labelled data
- Distillation from a large model to a smaller one
- Quantisation to fit your existing hardware
- Retrieval and prompt improvements where they beat retraining
- Before and after report with the numbers that decided it
Matokeo unayopaswa kutarajia
Matokeo unayopaswa kutarajia
- Lower cost per inference at the same or better accuracy
- Response times that suit interactive use
- A smaller model that runs on hardware you already own
- An evaluation set you keep, so the next change is measurable too
Zana na mifumo
Zana na mifumo
- LoRA and QLoRA fine tuning
- Quantisation (GGUF, AWQ, GPTQ)
- Knowledge distillation
- vLLM and TensorRT
- ONNX Runtime
- Ragas and custom evaluation harnesses
Tunavyofanya kazi
Tunatoa vipi
Tunatumia michakato bora na mbinu za uendelezaji kama msingi wa kujenga suluhisho za teknolojia za kisasa kwa njia iliyopangwa na yenye mpangilio.
- 01
Planning
We collect the relevant information from you and your stakeholders, including the constraints you have not written down.
- 02
Defining
We document the software needs, agree acceptance criteria, and write down what is out of scope.
- 03
Designing
System and interface documents are prepared against the specification: architecture, data model and stack.
- 04
Building
We implement in short cycles, with a working increment and a visible demonstration at the end of each.
- 05
Testing
We evaluate quality to find and fix defects: automated where possible, exploratory where judgement matters more.
- 06
Deployment
Release runs through an automated pipeline, checked for deployment issues, with a rehearsed rollback.
- 07
Maintenance
We keep the system current, secure and monitored, so you hear about problems from us first.
Maswali
Maswali yanayoulizwa sana
How much can costs actually come down?
It depends on the task, and we will not promise a number before measuring. For narrow extraction and classification work, a tuned small model replacing a frontier model commonly cuts cost by an order of magnitude.
Will accuracy drop?
That is what the evaluation set is for. We keep a change only if it holds up on your cases. A smaller model tuned on your data frequently beats a larger general one on a narrow task.
Can you optimise a model we already deployed?
Yes. We start by measuring the current setup, so any improvement is demonstrated against a baseline rather than asserted.
Huduma zinazohusiana
Huduma zinazohusiana
Tuongee
Hebu tuandae faida yako ijayo.
Tuambie unachotaka kufikia. Utazungumza na mhandisi, si kituo cha simu: kwa kawaida ndani ya dakika chache.
Jumatatu hadi Ijumaa, 08:00 hadi 18:00 Saa za Afrika Mashariki (GMT+3). Msaada wa dharura nje ya saa hizi kwa makubaliano.