Trusted by Forward-Thinking Enterprises Worldwide
QSS AI Labs is our dedicated applied-research practice, built to close the gap between frontier AI capability and enterprise-grade software. We experiment with large language models, autonomous agents, multimodal systems, and edge inference — and ship the results as measurable, deployable products.
Trusted by Forward-Thinking Enterprises Worldwide
QSS AI Labs is staffed by engineers who ship. We run active experiments every quarter, contribute to open-source projects, and partner with universities and research groups to stay ahead of the curve. When you engage the Lab, you tap into a living portfolio of techniques that have already been pressure-tested against real enterprise constraints.
Talk to a Lab ResearcherEight focused research streams map to the capabilities most enterprise teams are struggling to operationalize today. Each stream is led by a dedicated principal engineer with a published experiment roadmap.
We adapt open-weight and closed-weight LLMs to domain-specific tasks using LoRA, QLoRA, instruction tuning, and preference optimization — measured with rigorous evaluation harnesses, not vibe checks.
Multi-step, tool-using agents that plan, reason, and act across enterprise systems. We specialize in planning loops, memory design, guardrails, and human-in-the-loop checkpoints that keep autonomy safe.
Unified pipelines that reason across images, audio, and text — from document understanding and video analytics to voice-native assistants that stay in lock-step with a visual workflow.
Enterprise RAG architectures that combine hybrid retrieval, re-ranking, structured filters, and grounded citations — so answers are traceable, auditable, and safe to expose to regulated users.
Quantization, distillation, and compiler-level optimization that push models onto phones, wearables, cameras, and factory gateways — preserving privacy and slashing cloud inference costs.
Detection, segmentation, tracking, and visual QA models trained for the messy realities of industrial cameras, medical imagery, and real-world lighting — not just curated benchmarks.
Low-latency speech-to-text, natural text-to-speech, speaker analytics, and voice agents that can hold a conversation in noisy, real-world acoustic environments without breaking down.
When real data is scarce, sensitive, or expensive, we generate privacy-safe synthetic datasets and simulation environments — validated for distribution fidelity before a single model is trained on them.
Every engagement flows through the same four-phase loop. Each phase has concrete deliverables and an explicit go / no-go decision before the next phase begins.
We map your problem against the current AI landscape, survey relevant research, and define sharp hypotheses, success metrics, and evaluation datasets before a single line of training code is written.
Rapid two-to-four-week spikes build working prototypes against your real data. We compare architectures head-to-head and produce reproducible notebooks, model artifacts, and interactive demos.
We stress-test the prototype on bias, safety, latency, cost, and robustness. Red-teaming, adversarial evaluation, and business KPIs all roll into one decision-ready validation report.
Validated experiments graduate into deployable services — containerized, observable, CI/CD-ready, and paired with an MLOps runbook so your own team can maintain and extend them confidently.
Our research streams are sharpened against real client problems in regulated, high-stakes industries — not academic toy datasets.
Radiology assistants, pathology triage, clinical summarization, and privacy-preserving model training on protected health information.
Real-time fraud scoring, transaction pattern mining, and explainable risk models that regulators and compliance teams can actually audit.
Embedding-based recommendations, generative merchandising, dynamic pricing experiments, and multimodal search across catalog imagery and copy.
Route optimization under uncertainty, demand forecasting, warehouse computer vision, and autonomous dispatch agents that coordinate across systems.
High-speed defect detection, process-control anomaly spotting, and predictive maintenance models that live at the edge of the shop floor.
Document intelligence, secure multilingual translation, and decision-support agents designed to run in air-gapped or sovereign-cloud environments.
Private RAG assistants grounded in policy, contract, and engineering repositories — with per-user access control and citation-first outputs.
Our scoping workshop can shape almost any enterprise AI question into a measurable research sprint. Let’s map it together.
Most AI engagements stall between a slide deck and a deployed system. The Lab is engineered to get you across that gap.
Every researcher on the Lab has shipped production code. Experiments are designed from day one to survive latency budgets, uptime targets, and real compliance reviews.
Pods combine ML scientists, data engineers, domain experts, and senior full-stack engineers. You get a single team accountable for research, code, and deployment — no handoff cliffs.
Every experiment ships with pinned dependencies, versioned datasets, training logs, and an evaluation harness so results can be rerun a year later on a different cluster.
Bias audits, safety evaluations, prompt-injection red-teaming, and privacy impact assessments are built into the delivery checklist — not bolted on after go-live.
Time-zone-overlapping pods spanning the US and India mean research moves every 24 hours, not every business day, without sacrificing clear single-threaded accountability.
QSS has delivered mission-critical software for a decade and a half. The Lab inherits that discipline — security reviews, SOC2-aligned processes, and long-term support built in.
Quick answers to the questions enterprise teams ask us most when scoping a Lab engagement.
QSS AI Labs is our in-house applied research practice. It pairs AI engineers, ML scientists, and domain experts to explore emerging technologies such as large language models, autonomous agents, multimodal systems, and edge inference — and to turn the most promising experiments into production software.
Consulting usually delivers slideware. The Lab delivers working prototypes, evaluation harnesses, and deployable code. Every engagement is built around reproducible experiments, measurable KPIs, and a clear path from proof-of-concept to production.
Yes. We offer short 4-to-8-week research sprints focused on a single hypothesis — such as a RAG architecture for your documents, a voice-agent benchmark, or a fine-tuning comparison. You receive a working prototype and a decision-ready report at the end of the sprint.
Where clients agree, we publish method write-ups, benchmarks, and generic tooling on our engineering blog and GitHub. Client code, data, and results stay fully confidential by default, and any publishing requires explicit written approval.
You do. Unless otherwise agreed in writing, all custom code, model weights, prompts, datasets, and evaluation artifacts produced during a paid engagement are assigned to the client. QSS retains only general methodology and reusable internal tooling.
A scoping workshop typically runs within one week of first contact. Research sprints usually kick off within two to three weeks of contract signature, depending on data access, security review, and team composition.
Book a free 30-minute scoping session with a Lab principal. We’ll pressure-test the problem, outline a research sprint, and leave you with a one-page plan — whether or not we end up working together.