We build AI virtual assistants for SaaS product teams and enterprise customer-experience leaders, from chat support assistants and knowledge-base RAG layers to multimodal assistants that handle text, voice, and vision inputs in the same conversation. With 13+ years of production engineering experience and ISO 9001 certified delivery, we ship assistants that move past demo and into the eval-harnessed, observability-wired reality of production AI across 45+ countries.
Whether you are launching a conversational layer on a new SaaS product, replacing a static FAQ with a real assistant, or building a multimodal support flow that captures voice and images alongside text, our AI engineering team ships assistants that move the metrics that matter: deflection rate, resolution accuracy, and cost per conversation.
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Conversational assistants that handle tier-one support volume. Order status, account questions, billing queries, product how-to, returns and refunds. Trained on your knowledge base and conversation history, not on a generic model alone. Escalation paths to human agents when the assistant is not confident. Confidence-routed deflection rates of 40 to 60 percent in the first ninety days are realistic when the design is right.
Pre-sales assistants that qualify leads, answer pricing questions, book demos, and route warm prospects to a human seller. Onboarding assistants that walk new users through product setup, surface the right next step, and detect drop-off before churn risk shows up in your dashboard.
Retrieval-augmented assistants that answer strictly from your indexed content. Documents, knowledge base articles, internal wikis, product specs, policy manuals. The assistant cites its sources so users can verify, and your team can audit which content is and is not being used.
Employee-facing assistants for IT helpdesk (password resets, software access, device questions) and HR self-service (leave balance, policy questions, benefits guidance). Reduces internal ticket volume and gives employees instant answers during off-hours.
Assistants that combine inputs. Users can type, send a voice note, upload a photo, or share a screenshot in the same conversation. Useful for product support where users show what is broken, for medical-adjacent intake flows, and for field-service assistants that read equipment labels via the camera.
Sixty-minute call. We learn your product, users, content sources, and constraints. We agree on the assistant scope, the channels it will live in, and the evaluation criteria.
We propose AI engineers from our vetted bench. You interview and approve each one. The team plugs into your tools.
Two-week sprints with demos, eval reports, and code reviews you can audit. Eval matters more than vibes; we build the eval harness on day one.
Production readiness review, observability wiring, runbook handover, ongoing eval against live traffic.
Production AI assistants need an eval harness, an observability stack, fallback handling, and a confidence-routing strategy. We ship all four from day one. Demos that look great and break in production are how AI projects die.
Most teams get RAG wrong by treating it as a vector search plus a prompt. We design the chunking strategy, the retrieval scoring, the source citation flow, and the eval pipeline that catches retrieval drift before users do.
We work with OpenAI, Anthropic, Google, and open-source models. We pick the one that fits your latency, cost, and privacy requirements, not the one we have a partnership with. Cost-per-conversation maths is part of every scope conversation.
Documented engineering process. Clutch and GoodFirms verified reviews. India-rate billing with Western-grade engineering discipline. You get the cost arbitrage without the quality compromise.
Prompt versioning, eval datasets, observability dashboards, runbook for when the model provider has an outage. The work that makes a production assistant boring to operate is the work we own from day one.
Production AI assistants need an evaluation framework before they touch real users. We build the evaluation pipeline on day one of every engagement, not at the end. Three layers matter most.
Did the assistant understand what the user asked? We score this against a golden dataset of representative queries reviewed by your domain experts. Target accuracy is at least eighty-five percent for production support and ninety-five percent for transactional flows.
Did the answer say something true? We run automated checks for factual grounding against the source documents, plus human review on a weekly sample of one hundred to five hundred conversations. Hallucination rate target is below one percent on grounded queries.
Did the user leave with the answer they came for, and what did the conversation cost? We track resolution against your business definition (ticket avoided, lead qualified, account self-served) and unit cost against your runtime budget. The CFO metric.
| Model | Best for | How billed |
|---|---|---|
| Dedicated team (most common) | Multi-month roadmap with shifting priorities. You have product management capacity, we provide AI engineering velocity. | Per engineer per month, $1,200 to $4,000 depending on seniority. Three-month minimum. |
| Fixed-scope project | Defined deliverable like a v1 support assistant or a knowledge-base RAG layer. | Lump sum with milestone payments. Best when scope is genuinely fixed. |
| Staff augmentation | Adding named AI engineers to your existing team. | Per engineer per hour, $13 to $25 by seniority. |
| Time and materials | Discovery, prototypes, exploratory builds where the eval criteria are still being defined. | Hourly with a monthly cap. |
| What you might compare | Where ScalaCode fits | When it might not |
|---|---|---|
| US-based AI consultancy | India delivery rates, Western-grade engineering, ISO 9001 certified. We beat the US agency rate by three to four times for equivalent quality. | If procurement requires a US contracting entity and onshore data residency, contracting is more involved. |
| No-code chatbot platform (Intercom Fin, Ada, Forethought) | We build the custom layer where these platforms stop. Deep RAG, custom eval, multimodal, in-product context awareness. | If a no-code platform genuinely covers your use case, do not pay for custom. We will tell you when that is true. |
| Voice-first agent vendor (Retell, Vapi) | We work alongside these for voice channels. Our home turf is the chat and multimodal layer. | If the use case is voice-first end to end, the voice-specific vendors may fit better. |
| Building in-house | Faster time to v1 and lower fixed cost than hiring a five-engineer AI team. Useful when you do not yet have an AI lead. | If you already have a senior AI engineer and want to staff up around them, partnership may suit better than a full team. |
We integrate with what you have rather than asking you to migrate.
$13-15/hr
$18-20/hr
$23-25/hr
$1,200-$1,500/month
$1,800-$2,200/month
$2,400-$2,800/month
$3,200-$4,000/month
Per-conversation runtime cost depends on the model and the RAG depth. Frontier APIs run roughly three to twenty dollars per thousand conversations. Self-hosted open-source models run roughly twenty to sixty cents per thousand at steady state, plus the hosting cost.
I looked around at several developers to compare costs, but they didn’t fit within my budget. Finally, I reached out to a company in India called ScalaCode. We set up several online meetings over a couple of weeks and came up with an app that did exactly what I wanted within my budget. I can confidently say that ScalaCode has been an excellent choice for me.
Ruddy McKenzie
Founder of RM EPOSIn this heartfelt testimonial, James Ellis, the founder of TipStars, shares his transformative experience working with ScalaCode. He highlights how ScalaCode's expert team helped turn his vision of a tipping platform for artists and art lovers into a reality. James praises their innovative approach, dedication, and seamless project execution, which played a crucial role in the success of TipStars. This platform now empowers artists and enhances the experience for art enthusiasts, thanks to ScalaCode's exceptional development skills.
James Ellis
Owner, Artist-Tipping PlatformScalaCode provides great results, uplifting the collaborative experience with their impressive project management style. The team always delivers as expected, which is manifested by the length of the ongoing relationship with us. Overall, their services have been impressive.
Jaa St. Julien
Pres. & Chief Strategy Officer - St. Julien CommunicationsI have been working with ScalaCode for almost a year and half now. I have this project 4Sale, it’s a marketplace application. I contacted them for the project and we started around 2021. The company is very responsive and always take the extra mile to help you out. I highly recommend them; if you have a project, contact this company. They always respond on time even though there’s a time difference.
Manuel
CEO, 4SaleThe application was basically built from scratch, and was complicated, as the software was to be integrated with a certain Medical EHR software. As the CEO of SHG, I was very pleased with the services, expertise, and support we received from ScalaCode, from the beginning directly through the first LIVE implementation.
Stephen Holmes
CEO, Steve Homes GroupThe iOS and Android apps exceeded the expectations of the internal team. ScalaCode crafts high-quality products that are easy to use and fit the requirements of the client. The team is technically experienced, hard-working, and knowledgeable.
Carolyn Dare
Director, Empowered AchieverI needed a reliable team on-hand, and ScalaCode delivered. Their excellent availability and project oversight made a big impact.
Faid Lalji
Learn ArenaOur XR project had unique hurdles, but ScalaCode grasped it fast and delivered beyond expectations with excellent collaboration.
Alessandro
CEO / Founder (XR Company)A virtual assistant answers, routes, and guides. An agent takes action on your behalf, often across multiple tools and systems. Both use LLMs. The engineering depth and risk profile of an agent are higher because it executes transactions or workflow steps. We build virtual assistants here. For agents that act inside Salesforce, ERPs, or transactional systems, see our AI Agent Development page.
Not as our primary lane. We build chat-first assistants and multimodal assistants where voice is one input mode alongside text and vision. For voice-first end-to-end deployments, voice-specific platforms tend to fit better. We can integrate with those platforms when the wider experience needs both lanes.
Retrieval-augmented generation against your indexed content, source citation in every response, eval datasets that catch known failure modes, confidence routing that hands off low-confidence answers to a human or a fallback flow. Hallucination cannot be reduced to zero but it can be designed down to acceptable levels for production use.
v1 chat assistant with basic RAG: six to twelve weeks. Production assistant with eval, observability, multiple channels: twelve to twenty-two weeks. Multimodal assistant: fourteen to twenty-six weeks. Production hardening adds four to eight weeks regardless of tier.
Yes for read-side integration (pulling content and context into the assistant). Salesforce, HubSpot, Zendesk, Intercom, Notion, Confluence, custom knowledge bases. For write-side actions (creating tickets, updating CRM records, executing transactions), we can do this with the right guardrails but the scope crosses into agent territory and the eval requirements grow.
You do. IP assignment is in the master service agreement. Conversation data ownership and processing rules sit in a data processing addendum aligned to your platform’s privacy commitments. We set access controls appropriate to your compliance requirements (SOC 2, GDPR, HIPAA, depending on your context).
Usually within days. We agree on the scope, you interview and approve the engineers we propose, and they join your tools. The team comes from our existing vetted bench so there is no hiring delay. Discovery to first shipped sprint typically runs three to five weeks.
Build cost runs $20K to $250K depending on scope. Per-conversation model cost runs three to twenty dollars per thousand conversations on frontier APIs and twenty to sixty cents per thousand on self-hosted open-source. Infrastructure, observability, and eval maintenance typically add twenty to forty percent of build cost per year in steady state.
ScalaCode has been consistently available, day or night. Their hands-on support throughout the journey made them a true partner.
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