An assistant should know when to answer, and when to hand over.
Conversational AI connected to approved knowledge and business workflows, with evaluations, access controls and human escalation designed into the service.
Clear scopeReviewable deliveryPlanned handover
Niva is PySquad’s production assistant. Use it to see a working conversation, then scope the assistant against your knowledge, permissions, and handover rules.
Review NivaStart with the reason
Is this the work you need next?
A convincing demonstration is a starting point. Production usefulness depends on source quality, what the assistant is allowed to do, and how it behaves when it cannot answer reliably.
Users need help navigating a product or internal process
A chatbot prototype needs measurable production behavior
A useful starting scope pairs one of these needs with an accountable owner and a way to review the result.
The engagement, made tangible
Know what you are investing in.
Open a workstream to see its outputs. The proposal defines which artifacts and implementation work belong in your engagement.
Workstream 01
Use-case and conversation design
Select the task, intended users and boundaries of assistance.
- Task and channel scope
- Escalation journeys
- Representative evaluation questions
Bring this into your briefDiscuss this workstream
Choose with the trade-offs visible
There is more than one way forward.
A sound recommendation depends on your constraints. The choice is explicit before it becomes an implementation assumption.
Configure an existing platform
Consider this when established channels and workflows cover the need.
What to weigh
Review data handling, integration limits and ongoing platform costs.
Build a custom assistant
Consider this when product behavior, permissions or integrations require deeper control.
What to weigh
Budget for evaluation, maintenance and model changes alongside the build.
See the whole engagement
The handoffs matter as much as the build.
Before implementation, identify the decisions, systems, and owners at each boundary. This map is a discussion framework, refined around your environment.
- 01
User and access context
- 02
Approved knowledge
- 03
Answer or permitted action
- 04
Evaluation and human handover
How the work progresses
A decision at every milestone.
Use working evidence to review progress. Dates are agreed after scope and dependencies are understood.
Define
Agree on the problem, users, and constraints.
Decision evidence
An approved scope and acceptance criteria
Make tangible
Review the experience, contracts, or operating model.
Decision evidence
A reviewed design and dependency plan
Build and review
Implement in increments with visible progress.
Decision evidence
Working outputs checked against the scope
Release and transfer
Prepare operation, adoption, and ownership.
Decision evidence
Release approval and agreed handover assets
Quality and business value
Two questions before you call it done.
Does it behave as agreed, and is it creating the change you intended? Review both with evidence appropriate to the service.
Delivery evidence
Is the work ready?
Known-answer and unanswerable-question evaluations
Unauthorized-access and tool-action tests
Escalation and source-refresh review
Value signals
Is the change useful?
Task success on reviewed examples
Unsupported-answer rate
Human handover quality
Agree on definitions, a baseline, and a review period. These are suggested measures, not promised results.Designed for continuity
Plan the handover before the handover.
Ownership should be understandable throughout the engagement. Record the assets, access, and responsibilities your team needs after delivery.
Explore how we workProject assets
Identify source, designs, configuration, and documentation in the agreement.
Accounts and environments
Agree on account ownership, access roles, and credential handover.
Operational knowledge
Document routine tasks, recovery steps, and known limitations.
Ongoing responsibility
Define what your team owns and what support remains in scope.
Make the commercial conversation useful
What shapes the investment?
A credible estimate follows the work. These are the factors we clarify before proposing scope and delivery commitments.
01
Knowledge quality and access model
Confirm during scoping02
Number of channels and action integrations
Confirm during scoping03
Evaluation and operating requirements
Confirm during scopingBring a current system overview, representative workflows, and any fixed constraints. We can identify where discovery is needed and where a build can be estimated directly.
Request a scoped estimateHow we work together
Choose ownership, then the team.
Agree on who prioritizes work, reviews decisions, and accepts delivery. The engagement model follows that responsibility.
A defined engagement
For a bounded piece of work with clear outputs.
Agree on milestones, dependencies, and change handling.An ongoing product team
For a product or platform with a continuing roadmap.
Maintain shared priorities, review cadence, and release ownership.Embedded capability
For teams that already lead delivery and need additional expertise.
Align contributors to your engineering standards and review practices.Evaluate the people behind the promise
Bring the same scrutiny to your delivery partner.
Review published work, then ask us to connect relevant experience to your scope, constraints, and expected outcomes.
Before you decide
Clear answers. A better brief.
What does PySquad's AI chatbot solutions service include?
As an AI chatbot development company, PySquad delivers end-to-end chatbot engineering: use-case discovery, conversation design, multi-agent architecture, knowledge base and RAG setup, CRM and ERP integrations, analytics, guardrails, and production deployment.
Do you build custom chatbots or implement existing platforms?
Both. We deliver custom conversational AI when your product needs deep control over logic, data, and UX. We also implement production-ready platforms when speed to launch matters. Discovery defines the right path for your AI chatbot for business goals and roadmap.
Can you integrate chatbots with our existing website, SaaS, or ERP?
Yes. We embed widgets and APIs into websites and customer portals, connect to Odoo and other ERP systems, wire CRM and support tools, and build backend services so conversations trigger real workflows, not just scripted replies.
Do you support multi-language and omnichannel chatbots?
Yes. Programs can include multilingual models, channel adapters (web, mobile, messaging platforms), and consistent knowledge across touchpoints so customers get the same quality of answers everywhere.
How do you handle data privacy, security, and compliance?
We design with tenant isolation, access controls, audit trails, and data residency requirements in mind. For regulated industries we align chatbot architecture with your compliance lead on retention, PII handling, and evidence collection.
How long does it take to launch an AI chatbot?
Platform-based rollouts can go live in days when knowledge and channels are ready. Custom chatbot MVPs typically land in 6 to 12 weeks depending on integrations, data sources, and compliance scope. We ship in increments so you validate with real users before scaling.
What industries do you build chatbots for?
Healthcare, logistics and supply chain, manufacturing, retail and e-commerce, travel and hospitality, marina, insurance, plus SaaS and other verticals where domain-specific answers, compliance, and system integrations matter.
Can PySquad help with broader AI beyond chatbots?
Yes. Conversational AI is covered by this service. Broader applied AI work, such as document intelligence, classification, and workflow automation, is scoped separately based on your product goals.
Your next move
Define the work worth doing.
Choose where you are and what to discuss. We carry that into the enquiry for AI assistants & chatbots.