AI Services
Quinoid builds generative AI solutions around a specific job — assistants answering from your docs, content tools, workflow copilots, not generic chatbots.
Before you choose a partner
For teams adding document assistance, content generation, or conversational features with clear boundaries and human oversight.
If the process is predictable, rules-based automation may be easier to operate than AI. Start with a feasibility review when data quality, accuracy requirements, or the business benefit is uncertain.
Bring one workflow, examples of current inputs and outputs, permitted data access, and the errors your business can or cannot tolerate. Define how a human should intervene.
Share what you know now. We can clarify the remaining questions together.
Start with context
The work, in plain language
Generative AI solutions only earn their keep when they're built around a specific job, not a generic chatbot bolted onto a website. Quinoid's India-based teams build AI assistants that answer questions against your actual product documentation, content tools that draft against your brand's real style guide, and workflow copilots embedded in tools your team already uses — Slack, your internal CRM, your ticketing system. The hard part isn't calling a large language model API; it's retrieval — getting the right internal documents, tickets, or records in front of the model at the right moment — and evaluation, knowing whether the assistant's answers are actually correct before your customers or employees see them.
We build retrieval pipelines over your existing knowledge base, set up guardrails against hallucinated answers on factual queries, and run evaluation suites before anything ships to production. For teams building assistants, content generation tools, or copilots that need to be trusted by real users — not just demoed once — that evaluation discipline is what separates a generative AI solution that gets adopted from one that gets quietly abandoned after launch.
We ask the practical questions early, involve the people who use the product, and keep the work visible as it develops.
Explore the client’s challenge, our contribution, and the delivered solution.
Focused help, shaped around the part of the journey you are in.
We build chat assistants that retrieve answers from your actual help center, product docs, and past tickets, with citations back to source documents so support teams can verify answers before they're trusted.
Wikis, Slack history, PDFs, and internal tools get indexed into a single searchable assistant, so employees stop pinging colleagues for answers that already exist somewhere in the company.
We fine-tune prompts and retrieval against your actual brand voice, past campaigns, and approved messaging, so drafts need light editing rather than a full rewrite before publishing.
Copilots that draft replies, summarize records, or pre-fill forms get built directly into your CRM, ticketing system, or internal admin panel, so adoption doesn't require a new tool or login.
Small, visible steps keep decisions timely and surprises rare.
We define exactly what the assistant must and must not do, including which queries should be refused or escalated to a human rather than answered automatically.
We index your actual documents, tickets, or records into a retrieval system, then test it against real user questions before connecting any generation model.
We design prompts, output formatting, and refusal logic together, then add guardrails that catch hallucinated facts on queries where accuracy is non-negotiable.
We build a test set from real user questions and score the assistant's answers for accuracy and tone before any production rollout, not just a one-time demo.
We launch to a limited user group first with a feedback loop and human review of flagged answers, then expand access once accuracy holds steady.
Useful AI tools grounded in your business data
Faster knowledge work without losing oversight
Clear evaluation loops before scaling AI usage
We treat generative AI accuracy as something you measure, not something you assume. Every assistant we build ships with an evaluation set drawn from real questions, source citations users can verify, and a clear boundary for what it will refuse to answer.
We build a documented evaluation suite from real user questions before launch, not just a single demo run.
Assistants cite source documents inline so users and support teams can verify answers instead of trusting them blindly.
We define explicit refusal and escalation logic upfront, so the assistant hands off to a human rather than guessing on high-stakes queries.
We ground responses in retrieval from your actual documents and add citations so answers are traceable. For factual or high-stakes queries, we add guardrails that force a refusal or escalation rather than a guess.
Yes. Most projects start by indexing your existing help center, wikis, or internal documents directly — we rarely need new content written before building the retrieval layer.
We select the model based on your latency, cost, and data-residency requirements, often a current large language model or an open-weight model run privately, rather than locking into one vendor by default.
Source readiness, supported tasks, evaluation criteria, integrations, and human review determine the schedule. Agree a limited pilot and its acceptance criteria before planning production rollout.
Plan the next step
