ScalaCode builds and deploys production AI chatbots, support, sales, lead qualification, internal helpdesk, and conversational commerce bots powered by GPT-5, Claude, Gemini, and self-hosted open-source models, for enterprises across 45+ countries. With 13+ years of conversational systems experience, our teams take chatbots from prototype to live channel ownership, with the intent classification, fallback handling, and human-handoff design that determines whether users come back.
Whether you need a 24/7 support bot that resolves 60%+ of tier-1 tickets without escalation, a sales assistant that books qualified meetings on your CRM with confidence routing, or a multilingual conversational layer across web, WhatsApp, and Microsoft Teams, our chatbot engineers architect solutions that move the metrics that matter, containment rate, conversion lift, customer satisfaction.
LLM-powered chatbots that handle tier-1 customer inquiries across web, in-app, WhatsApp, Slack, Teams, Facebook Messenger, and SMS. Retrieval-grounded against your help center, internal knowledge base, and ticket history so answers are accurate and citation-backed. Escalation handoff to human agents with full conversation context. Integrated with Zendesk, Intercom, Freshdesk, Salesforce Service Cloud, ServiceNow.
Sub-second-latency voice agents built on OpenAI Realtime API, Vapi, LiveKit, Retell AI, or Cartesia. Inbound: customer support, appointment booking, IVR replacement, qualifying calls. Outbound: appointment reminders, follow-ups, surveys, lead qualification. Full call transcription, sentiment analysis, and CRM integration. Compliant with industry regulations (TCPA, GDPR, India DPDP).
Conversational lead capture and qualification flows that engage website visitors, qualify intent, book meetings, and route to sales reps. Personalised based on behaviour signals (pages visited, time on site, returning visitor flag). Integrated with HubSpot, Salesforce, Pipedrive.
Conversational AI for internal use cases: HR-policy-Q&A bots, IT-help-desk bots, employee-onboarding bots, finance-policy-Q&A bots. Integrated with Workday, BambooHR, ServiceNow, internal SharePoint / Confluence / Notion. Behind SSO and RBAC, so employees only see content they’re authorised for.
Conversational shopping assistants for product discovery, gift recommendation, sizing guidance, and post-purchase support. Integrated with Shopify, Adobe Commerce, BigCommerce, Salesforce Commerce Cloud. Often paired with our AI recommendation engine for personalised conversations.
Patient triage chatbots, appointment-booking bots, medication-reminder bots, post-visit follow-up bots. HIPAA-aligned with PHI isolation. Integrated with Epic, Cerner, athenahealth, or custom EMR systems via FHIR.
Wealth-management conversational interfaces, banking customer-service chatbots, claims-status chatbots for insurance, fraud-alert conversation flows. SR 11-7 aligned. Audit trails on every interaction.
Chatbot interfaces embedded inside SaaS products as in-app copilots. Conversational analytics inside dashboards, conversational config inside admin panels, conversational support inside the product itself. Increasingly the default UX pattern for SaaS products in 2026.
Chatbots that speak 30+ languages with natural-sounding multilingual responses, code-switching support (Hinglish, Spanglish, Arabglish), and cultural-context awareness. Built on multilingual LLMs (GPT-5, Claude Sonnet 4.6, Gemini 2.5, Llama 3.3) without per-language model fragmentation.
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Smart routing across GPT-5 / Claude Sonnet 4.6 for nuanced or sensitive conversations and fine-tuned Llama 3.3 / Mistral for high-volume tier-1 deflection. Routing decisions evaluated and refined over time. Typical cost reduction: 60 to 80% vs always-frontier-model architectures.
Same backend reasoning engine serves both voice and text channels, no parallel implementations. OpenAI Realtime API for voice; OpenAI Assistants API or LangGraph for text. Channel-specific adapters handle latency, formatting, and turn-taking differences.
Chatbots that remember what the user discussed last week, last month, last year, for sales, customer success, healthcare, and any long-relationship use case. Built with vector + structured memory architectures that respect privacy and consent boundaries.
Real-time sentiment scoring on every customer message, escalate to human when frustration detected, change tone when positive, surface internal alerts when high-value account expresses dissatisfaction. Integrated with our sentiment analysis solutions.
Chatbot interfaces embedded inside SaaS dashboards as ambient copilots, context-aware about what the user is doing, suggesting next actions, generating reports on demand. Increasingly the default UX pattern for B2B SaaS in 2026.
High-confidence responses ship to user immediately. Mid-confidence responses get a “let me verify” pause + supervisor agent check before user sees them. Low-confidence escalates to human. Dynamic threshold tuning lets containment rate climb safely over time without quality regression.
Customer starts a conversation on web chat, continues on WhatsApp the next day, escalates to a phone call, all stitched into one conversation history. Identity resolution + conversation continuity across channels is a 2026 production-table-stakes feature.
Need conversational AI expertise embedded in your own team? We staff senior chatbot engineers with 3+ years of production conversational system experience.
Before building, we map exactly what the chatbot is responsible for, what it must defer to humans, what tools it can call, what success looks like (containment rate, CSAT, deflection percentage). Most chatbots that fail in production were never properly scoped.
Where does the chatbot live? Web widget, in-app, WhatsApp, Slack, Teams, voice, each has different latency budgets, formatting constraints, escalation patterns, and analytics models. Multi-channel deployments need a unified backend so behaviour is consistent across surfaces.
Production chatbots ground every factual answer in retrieved content from your knowledge base, not just model priors. We integrate RAG pipelines directly into chatbot reasoning so every claim has a citation back to source material. Critical for enterprise customer support and regulated industries.
Chatbots that take actions, book a meeting, look up an order, update a ticket, process a refund, call enterprise tools through Model Context Protocol or direct API integration. Integration depth lives on our AI integration services page; chatbot tool-use design lives here.
Persona, tone, response length, formatting, error-handling style, escalation phrasing, all designed deliberately. Production chatbots feel like the brand, not like a generic LLM. We build prompt frameworks + few-shot example libraries that encode your brand voice without per-response prompt overhead.
Every production chatbot has explicit escalation triggers (low confidence, regulated topics, frustrated users, request for human, repeated failures). Handoff happens with full conversation context, customer record, and recommended actions. Human agents receive the chatbot’s reasoning, not just the transcript.
Voice agents need additional engineering: low-latency streaming (sub-500ms target), interruption handling, turn-taking design, voice cloning where appropriate, telephony integration (Twilio, Vonage, Plivo), call quality monitoring, post-call summarisation. Different problem space than text chatbots; we treat voice as a first-class build, not a text-bot bolt-on.
Production chatbots ship with input guardrails (prompt injection, PII detection, off-topic filtering), output guardrails (toxicity, regulated-content filtering, hallucination detection), and brand-safety classifiers (does this response sound like our brand?). Built on Llama Guard, OpenAI Moderation, NVIDIA NeMo Guardrails, plus custom classifiers.
Chatbots get evaluated on the dimensions that matter, answer accuracy, response helpfulness, brand voice consistency, escalation correctness, latency, cost. Golden conversation datasets run automatically on every prompt or model change. No chatbot reaches production without passing the eval gate.
Every conversation is traced with model calls, tool use, latency, cost, sentiment trajectory, and outcome (resolved, escalated, abandoned). Weekly review identifies new failure modes, which feed back into the eval use and prompt updates. Production chatbots that work get better every week; ones that don’t have observability degrade.
We build chatbots like production systems, eval harnesses, observability, drift monitoring, escalation correctness testing, brand-voice classifier evals. Most chatbots we’re called in to fix were demos that got rushed into production without these foundations.
We design unified backends from day one when voice is on the roadmap. Voice-as-an-afterthought builds always end up with parallel implementations and drift between channels.
Persona, tone, response patterns, escalation phrasing, all engineered with prompt frameworks + brand-voice eval classifiers, not left to “hope the model picks up the vibe.” Production chatbots feel like your brand, not like generic GPT.
Chatbots that take real actions through Model Context Protocol or direct API integration to your CRM, ITSM, knowledge base, and custom systems. Standardised wiring vs bespoke connectors per integration.
Multi-language support is a first-class design concern, not a translation pass after launch. Conversational nuance, cultural context, code-switching all engineered for.
Conversation design, system architecture, model engineering, integration, deployment, change management, and ongoing operations under one roof.
In-product copilots, customer support deflection chatbots, sales-qualification chatbots, customer-success conversational analytics. Our default chatbot lift in SaaS: 40 to 60% tier-1 ticket containment, with CSAT held flat or improved.
Claims-status chatbots, policy-Q&A bots, broker-facing copilots, FNOL (first notice of loss) intake conversational flows. Voice-agent options for inbound claims hotline replacement.
Customer-service chatbots and voice agents for billing inquiries, service troubleshooting, plan changes, outage notifications. High-volume containment focus given the call volumes in this category.
Citizen-service chatbots for benefits Q&A, application status, document submission guidance. Multilingual + accessibility-first. Often deployed on sovereign-cloud or on-premises infrastructure.
Use-case audit, channel strategy, conversation design, MVP architecture, business case modelling. Starting at $20k-$40k.
Production-grade chatbot for one channel + one use case (e.g., web customer support, or WhatsApp lead qualification). Includes RAG, eval use, observability. $60k-$180k.
End-to-end chatbot platform across web + in-app + messaging + voice with shared backend, unified analytics, governance framework. $250k-$800k+.
Production voice agent on telephony or in-app voice, telephony integration, sub-500ms latency engineering, call quality monitoring. $80k-$250k.
Embedded squad, conversation designer, LLM engineer, integration engineer, MLOps engineer, running with your team for 6+ months.
Post-launch operations: prompt drift management, model updates, eval re-runs, new use-case onboarding, content updates, incident response. SLA-backed.
Support chatbot in Zendesk. 54% of tier-1 tickets resolved without human intervention. CSAT on chatbot-resolved tickets scored 0.3 points HIGHER than human-resolved equivalents.
Claims-status chatbot across web + WhatsApp + voice. Average inquiry resolution time 18 min → 2 min. Call-center deflection 41%. Customer NPS +14 points within 90 days.
Patient triage chatbot integrated with Epic. After-hours call volume to nurse line cut 67%. Patient satisfaction +0.4 on 5-point scale.
Conversational shopping assistant with voice + text. Conversation-led conversions +28%. Average order value on chatbot-led purchases +12%.
IVR replacement voice agent for billing inquiries. Containment rate 71%. Customer time-to-resolution cut 4.5 minutes per call. $11M annualised cost reduction.
Internal HR-policy chatbot. Employee question volume to HR team cut 73%. HR team time reallocated from FAQ-answering to strategic work.
The lines blur in 2026 but a useful distinction is: a chatbot’s primary job is to TALK with users (answer questions, deliver information, hold a conversation); an agent’s primary job is to DO things on behalf of users (take actions, traverse systems, execute multi-step workflows). A modern chatbot increasingly takes some actions, so most production chatbots are partly agents under the hood. The deeper agent architecture lens lives on our AI agent development page; this page is the conversational-interface lens. Most real programs need both, chatbot for the user-facing surface, agent for the backend reasoning that makes the chatbot able to actually solve problems.
Depends on the workload. GPT-5 and Claude Sonnet 4.6 deliver the strongest tool-use reliability and conversational nuance, use them where decisions matter and your data can be processed in the cloud. For high-volume tier-1 deflection on bounded topics, fine-tuned Llama 3.3 or Mistral on vLLM serving typically delivers 60 to 80% cost reduction with near-equivalent quality on your specific domain. Most production chatbots we ship use smart routing, frontier model for nuanced or sensitive conversations, fine-tuned open-source for high-volume deflection. Single-model standardisation is rarely the right answer at scale.
Five layers. (1) Retrieval grounding, chatbot answers cite real content from your knowledge base via RAG, not just model priors. (2) Confidence routing, low-confidence answers route to “let me verify” supervisor-agent checks or human escalation. (3) Output guardrails, toxicity / hallucination / regulated-content classifiers run on every response. (4) Eval use, golden conversation datasets exercise the full reasoning + tool-use end-to-end on every change. (5) Human escalation triggers, frustrated users, regulated topics, or repeated failures route to humans automatically with full conversation context. Combined, these get production chatbots to 90 to 95%+ answer accuracy on bounded domains.
Yes. We build native integrations with Zendesk (Sunshine API + Messaging), Intercom (Fin AI + custom apps), Freshdesk, Salesforce Service Cloud (Agentforce + custom Apex), ServiceNow (Now Assist + custom virtual agents), HubSpot, and more. For custom systems, we use Model Context Protocol or direct REST/GraphQL integration. The chatbot can read tickets, update records, query customer history, look up product details, process refunds, schedule callbacks, anything your systems expose via API. Integration depth lives on our AI integration services page.
A focused single-channel chatbot on a bounded use case typically reaches production in 6 to 10 weeks: 2 weeks discovery + conversation design, 3 to 6 weeks build + RAG + integration, 1 to 2 weeks shadow-mode validation. Multi-channel programs run 3 to 6 months end-to-end. Voice agents take an additional 2 to 4 weeks vs equivalent text chatbots due to telephony integration and latency engineering. Fastest credible timeline to a working production chatbot on a simple FAQ-deflection use case: 4 weeks.
Discovery + architecture sprints: $20k-$40k. Pilot chatbot for one channel + one use case: $60k-$180k. Multi-channel chatbot program (web + in-app + messaging + voice): $250k-$800k+ depending on integration scope, language coverage, and governance complexity. Voice agent builds add $80k-$250k on top of text. Ongoing infrastructure cost depends on conversation volume, most production chatbots land $0.01-$0.20 per conversation on optimised hybrid stacks. Most programs we’ve shipped pay back within 6 to 12 months on cost-per-conversation savings or revenue lift.
Voice has tighter constraints. Sub-500ms latency target (text tolerates 1-2s; voice does not). Streaming responses, model output streams into TTS as it generates rather than waiting for full completion. Interruption handling, user can interrupt mid-response and the agent must gracefully stop and switch context. Turn-taking design, knowing when the user is done speaking vs pausing. Telephony integration (Twilio, Vonage, Plivo) for inbound/outbound calling. Voice-specific evals, call quality, transcription accuracy, latency, post-call summarisation. We treat voice as a first-class build, not a text-bot bolt-on. Stacks: OpenAI Realtime API, Vapi, LiveKit, Retell AI, Cartesia.
30+ languages with natural-sounding multilingual responses on a single backend (no per-language model fragmentation). Built on multilingual LLMs (GPT-5, Claude Sonnet 4.6, Gemini 2.5, Llama 3.3 multilingual variants) plus multilingual embedding models (bge-m3, Cohere multilingual). Code-switching (Hinglish, Spanglish, Arabglish, Singlish) supported natively. Cultural context, politeness norms, expression conventions, formality registers, encoded in prompt design rather than assumed. Voice agents support 30+ languages on TTS side via ElevenLabs, Cartesia, OpenAI TTS.
Yes. For sovereignty, regulated, or air-gapped environments we deploy open-source LLMs (Llama 3.3, Qwen 3, Mistral, DeepSeek) on customer infrastructure using vLLM, Ollama, NVIDIA NIM, or Triton serving. RAG pipelines run on local vector stores. Channel integrations adapt, Slack/Teams on-prem, custom messaging, telephony via on-prem SIP. Frontier models (GPT-5, Claude) used only for non-sensitive reasoning steps where data can leave the perimeter; everything else runs locally. Healthcare PHI workloads, banking SR 11-7 model risk requirements, defence / public sector all routinely require this. We’ve shipped to AWS GovCloud, Azure Government, India MeitY-empanelled regions, and customer-owned datacenters.
Depends on the domain. For bounded customer-support categories with strong knowledge-base coverage (SaaS, telecom, e-commerce returns), 40 to 60% tier-1 containment is realistic and what we typically achieve in production. For complex regulated domains (banking, insurance claims, healthcare triage) where escalation thresholds are appropriately conservative, 25 to 40% containment is realistic. We design for containment growth over time, start conservative, tune dynamically based on observed quality, increase autonomy as eval coverage matures. Critically: containment without quality regression is the metric that matters. We measure both containment rate AND CSAT on chatbot-resolved tickets vs human-resolved baseline, and only push containment higher when CSAT holds or improves.
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