Data Science
Conversational and generative AI systems are chatbots, copilots, and assistants built on large language models with retrieval-augmented generation, so they answer from your knowledge base — securely, with guardrails, and in your brand voice.
Scope
The scope of every Conversational & Generative AI Systems engagement is two lists: the capabilities we engineer, and the artifacts your team keeps when the handover is done. Both are agreed before build starts, and the technologies below are the stack those lists are usually built on.
System blueprint
Answers with receipts: knowledge sources are indexed for retrieval, the assistant answers with citations under guardrails, evaluation gates every change, and conversation analytics reveal the gaps to index next.
Impact
Every engagement is framed the same way: the operating problem as we find it, the system that replaces it, and the baseline-to-target movement agreed in discovery — measured, not promised.
Support and internal questions queue for humans who answer the same thing daily, while the knowledge that could answer them sits unread in wikis, tickets, and PDFs.
An assistant grounded in your knowledge base that answers with citations, escalates with context, and shows you — through its gaps — what to document next.
Use cases
Select an industry to see how this service lands there, and in which sub-industries the impact concentrates. All 4 industry views are written out on this page — the tabs only change which one is in front.
In-product copilots grounded in product docs and tenant context, with evaluation suites that keep behaviour stable across model updates.
Docs-grounded assistants cut time-to-first-successful-call.
Copilots that act on tenant data under existing permissions.
Natural-language querying with citations to the underlying data.
Assistants that resolve with citations and escalate with context — containment rises without the brand-damage risk of an ungrounded bot.
Order-aware answers resolve the top intents end to end.
KB-grounded troubleshooting with escalation summaries attached.
Agent-assist drafting grounded per client knowledge base.
Policies, SOPs, and tribal knowledge become an assistant with sources — onboarding compresses and the same questions stop queuing for the same experts.
Policy answers with citations, escalating sensitive cases to humans.
How-to resolution before a ticket is ever created.
Procedure lookup by voice or phone at the point of work.
Shopping assistants grounded in catalog, inventory, and policy — answers that sell accurately and never invent a discount.
Buyer and seller questions answered from live listing data.
Guided selling in brand voice with real inventory awareness.
Retention conversations handled with policy-safe offers.
Methodology
Delivery runs in 5 documented phases, from Use Case Definition & Data Preparation through Monitoring, Feedback & Continuous Learning. Each phase lists its window, its work, and the psychological, adoption, and system challenges we plan for at that stage — naming them early is how they stay small.
We begin with the recognition of main business goals—customer support, content creation, or process automation—and the collection of domain-specific datasets to either train or fine-tune LLMs.
The establishment of models for certain purposes is carried out through the utilization of OpenAI GPT architectures, Llama 3, or Mistral.
Through the use of vector databases (Pinecone, FAISS), we establish retrieval-augmented generation (RAG) pipelines to embed the model in current enterprise data, leading to fewer hallucinations and increased factual precision.
Kubernetes deploys our conversational systems as microservices that can be positively scaled horizontally and easily integrated with current APIs, CRMs, and databases.
Each agent that has been deployed comes with tools for monitoring, including telemetry, analytics dashboards, and model evaluation metrics (BLEU, ROUGE, perplexity).
Delivery plan
Three views of the same engagement: when each phase runs, where the pod spends its effort, and the measures the work reports against. The windows restate the timeline quoted above — phases overlap by design.
Containment with citation
Conversations resolved by the assistant with a source shown, no handoff.
Escalation quality
Share of handoffs that arrive with correct context attached.
Eval pass rate
Evaluation suite pass rate on every prompt or model change.
Frequently asked
The 5 questions asked most often about this service, answered directly. Broader engagement questions — cost, ownership, and what happens after go-live — are answered on the services overview.
The production of chatbots, AI copilots, knowledge assistants, and LLM-driven enterprise automation tools is done by us, and these applications can be customized for either text or voicebased interactions.
Definitely. We fine-tune domain-specific LLMs using proprietary and open-source architectures (GPT, Llama, Falcon) tailored to your business domain.
Retrieval-Augmented Generation improves precision by linking LLMs to corroborated data sources, thereby making sure that the replies are both grounded and contextually right.
Definitely. Our approach allows for the development of API-based integrations to facilitate the smooth transfer of data between Salesforce, HubSpot, ServiceNow, and SharePoint, among others.
Data encryption, access control measures, and private deployment models are among the strategies we employ to ensure compliance with GDPR and SOC 2 standards.
Related
Most engagements combine two or three services — a data foundation under an analytics build, or MLOps under a computer-vision rollout. The full catalog of ten is on the services page; the closest siblings are below.
The first step is a scoping conversation about your use case, the data behind it, and what a production release must prove. It is technical, it is free, and it ends in a written recommendation.
Last updated: