Services
AI Research & Development
Applied AI research on custom architectures, domain fine-tuning and novel algorithms - delivered as time-boxed sprints with defined success criteria and a clear path to production.
AI Research & Development at a glance
- Sprint length
- 2-8 wk
- End-of-sprint report
- Written
- IP, code & weights
- You own
- To production
- Path
Some problems have no off-the-shelf answer. Our AI R&D group works on applied research - custom model architectures, domain-specific fine-tuning, and algorithm development - for organisations that need a capability their competitors cannot simply buy.
What we take on. Problems where an off-the-shelf model plateaus below the accuracy you need. Domains where the training data is unusual (medical imagery, Urdu text, industrial sensor streams, low-resource languages). Questions that need a novel loss function, retrieval strategy or model architecture, not just a bigger dataset.
How R&D is structured. Every engagement is a time-boxed research sprint with defined success criteria - typically two to eight weeks per sprint. At the end you get a written report, the code, the trained weights, and a clear recommendation: continue, pivot, or stop. There is no open-ended science-project mode with no exit ramp.
Fine-tuning open-source models. Llama, Mistral, Phi and Qwen on your proprietary data, quantised for cost-effective serving, deployed on your infrastructure - private cloud, on-prem, or edge. RAG pipelines when the answers must come from your documents, not the model's memory.
Novel architectures and algorithms. When existing architectures cannot fit your data or your latency budget, we design new ones - and prove they work with benchmarks against the incumbent before you commit to a production build.
Where R&D handshakes with production. When a research direction succeeds, the same team can carry it through to a production model, an integrated system and long-term monitoring. Nothing stays on a laptop.
Publication and IP. All intellectual property developed for you belongs to you. Where novel techniques emerge and publication makes commercial sense, we can co-author with your team - keeping credit and competitive advantage on your side.
Service walkthrough
See the work in a live session with the engineer who would run your project
Book a walkthroughResearch That Ships
Two-to-eight-week sprints, written go / no-go at every checkpoint, and full IP and weights handed to you. No open-ended science projects.
Research directions
Where R&D helps
- Custom model architectures
- Domain-specific fine-tuning
- LLM fine-tuning (Llama, Mistral, Phi, Qwen)
- Retrieval-augmented generation (RAG)
- Model quantisation & distillation
- Novel loss functions
- Low-resource language models (Urdu)
- Medical & scientific imaging
- Industrial sensor analysis
- Time-series forecasting research
- Reinforcement learning
- Explainability & bias research
How research runs
Sprint to publication
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1
Problem framing
We write down the research question, the success metric and the hard constraints, so everyone agrees what "worked" means before a single experiment runs.
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2
Sprint execution
A time-boxed sprint of two to eight weeks. Experiments run daily, results tracked, dead ends killed early so budget goes where it earns.
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3
Report & decide
End-of-sprint written report with results, plots, honest limitations and a recommendation: continue, pivot, or stop. You decide, with the data in front of you.
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4
Production handoff
When research succeeds, the same team hardens the model, wraps it in an API and integrates with your system. Wins move off the laptop and into your product.
Why HISOFT
Why R&D leaders pick us
Research budgets are limited and calendar-sensitive. We keep engagements tight, honest and reviewable - and we bring production engineering along, so a good result actually becomes a product.
- Time-boxed sprints, no open meters
- Written report at every checkpoint
- IP, code & weights go to you
- Production team on the same bench
Time-boxed, not open-ended
Every sprint has a defined length, budget and success criterion. If the sprint misses the metric, you get the honest write-up and a clean exit option, not an argument for more budget.
Research + engineering together
Research that never gets deployed is a hobby. Our R&D group sits alongside production engineers so a promising model moves to a hardened API in weeks, not months.
Your data stays yours
Training data, trained weights, code, notebooks and papers are all handed to you. We keep no residual rights and no shadow copies.
Publishable, when it makes sense
Where novel techniques emerge and publication is in your interest, we co-author with your team - subject to your review of every draft before submission.
Questions we hear often
Frequently asked
How is R&D different from AI Solutions?
AI Solutions applies known techniques to a business problem. R&D is what you commission when the known techniques do not reach the accuracy or capability you need - and you have to invent past them.
How is R&D priced?
By the sprint. Two to eight weeks per sprint at a fixed cost, with a written go / no-go decision at the end. No open-ended contracts.
What if the sprint fails to hit the metric?
You get the full write-up: what was tried, what worked partially, what did not, and what a next sprint would test differently. You then decide whether the upside justifies more investment. About one sprint in four ends in an honest stop.
Do you publish papers?
Where the client agrees and novelty warrants it, yes - co-authored with your team, subject to your review of every draft before submission.
What data do you need from us?
That is one of the first questions the sprint answers. Sometimes you already have enough; sometimes we help design a data collection or labelling exercise before real research can begin.
Do you take on very early-stage ideas?
Yes - a short feasibility sprint is a cheap way to test whether a bigger investment is warranted. We prefer this to jumping straight into a six-month engagement.
Which base models do you fine-tune?
Whichever fits your latency, cost and licence constraints. In practice: Llama 3, Mistral, Phi, Qwen and DeepSeek for open weights; and API-based models where the constraint is not sovereignty.
Ready when you are
Let's talk about your AI Research & Development project
Every ai research & development engagement starts with a short, no-obligation call. Tell us what you need and we'll come back inside one business day with a fixed quote or the next practical step.