Assistants that do the work, not just answer the FAQ.
We build chat and voice assistants that resolve real requests: they answer from your own data, take actions through your own systems, and hand off to a person the moment confidence drops.
Deployed where it pays — support, IT, HR, sales — on whichever model fits (OpenAI, Anthropic Claude, or Google Gemini), inside your own cloud. Every prompt, eval, and line of code is assigned to you.
Because they were demoed, not engineered. A scripted bot answers the five questions it was shown and breaks on the sixth; a model bolted onto a help center invents a refund policy that doesn’t exist. The deflection rate never materializes, and the project is quietly retired after a quarter.
The gap is never the model — today’s models are extraordinary at language. The gap is the engineering around it: grounding the assistant in your real data, wiring it to your systems of record, measuring whether it’s right before customers find out it isn’t, and knowing when to escalate.
That surrounding system is the entire job, and it’s what decides whether conversational AI development returns anything.
This isn’t one product — it’s a pattern that earns its keep in a handful of specific, high-volume processes. For each, what it does, the benefit it produces, and how that plays out:
Answers order status, account questions, troubleshooting, and returns 24/7, escalating to an agent only when the issue is genuinely complex. Benefit — faster responses and higher customer satisfaction at lower cost. First-response time collapses from hours to seconds, and routine volume stops queuing behind agents.
Example: a customer asking “where’s my order?” at 2 a.m. gets an instant, grounded answer instead of waiting for business hours — so the ticket never becomes a frustrated follow-up, and CSAT on those interactions rises.
Handles password resets, access and provisioning requests, and first-line troubleshooting for employees. Benefit — employees unblocked in minutes, and IT capacity reclaimed for real work. Mean-time-to-resolution on routine tickets drops, and the team stops spending its day on repetitive requests.
Example: a locked-out employee restores access in under a minute through the assistant instead of waiting in a ticket queue — saving the downtime and the IT hour it would have cost.
Answers policy, benefits, payroll, PTO, and onboarding questions from your own HR knowledge base. Benefit — instant answers for staff, and HR freed from repetitive Q&A.
Example: an employee checks their remaining parental-leave entitlement at the moment they need it instead of emailing HR and waiting a day — and HR stops fielding the same fifty questions a week.
Fields pre-sales product questions, guides plan selection, and books meetings on your site. Benefit — more qualified leads captured, with no after-hours drop-off.
Example: a prospect comparing plans at midnight is guided to the right tier and books a demo on the spot — a lead that a next-day callback would often have lost.
Processes returns, reorders, scheduling, and account changes, wired directly to your order and fulfillment systems so the assistant does the task. Benefit — lower contact volume and higher retention. Self-service on the routine actions removes friction that otherwise drives churn and call-center load.
Example: a customer reschedules a delivery or starts a return inside the chat instead of calling — resolving in seconds what would have been a five-minute call.
Lets frontline and multi-site staff query SOPs, manuals, and policy in plain language. Benefit — faster, more consistent frontline decisions and fewer errors.
Example: a store manager facing an unusual refund exception gets the exact policy in seconds instead of calling the regional office — so the call gets handled correctly the first time.
The scope below is the difference between an assistant that ships and a chatbot that gets unplugged.
We map where an assistant genuinely pays off and which channels matter (web, in-app, voice, messaging) — run as our AI readiness assessment, with the honest “don’t build this one” call included.
The assistant answers from your documents, policies, and product data — not from training-data guesswork — and every answer can cite its source. Grounding accuracy is measured against your own content before launch.
We connect it to your CRM, ticketing, order, and knowledge systems through structured, permissioned tool calls — so it can check an order or file a ticket, inside the access controls your security team already runs.
Where the use case calls for it, the same intelligence ships as a voice assistant and across the languages your customers speak.
Before a customer sees it, the assistant is tested against a golden set built from your real conversations — accuracy, tone, refusal behavior, the failure cases that must never ship. Human-in-the-loop handoff is designed in: it escalates instead of guessing when confidence drops.
We ship behind a staged rollout, instrument it for drift and cost, and train your team to read transcripts, maintain the evals, and tune the prompts.
What you get when you hire us — all assigned to you
The same delivery model behind all our AI development work, tuned for assistants — one accountable lead, fixed scope, no handoffs.
Scope the use case, channels, and the data the assistant must answer from.
Output: a ranked plan & the success metrics we’ll be judged on
Build the evaluation set from your real conversations and choose the model on your workload, not on hype.
Output: a golden test set & a grounding architecture
Develop the assistant in your own cloud tenant, wired to your systems through governed tools, with guardrails and escalation in place.
Output: a working assistant behind your access controls
Shadow mode, then a pilot, then wide — deflection and accuracy measured weekly, your team trained to operate it.
Output: a production assistant & a team that owns it
Most engagements reach production in 4–8 weeks, with full work-for-hire IP assignment signed at kickoff.
A customer-facing assistant is only as reliable as the production discipline underneath it — and that discipline is what we’re known for. The same process that holds a 200+ location restaurant business at twice-a-week releases with zero critical defects across four years is the one we bring to an assistant that talks to your customers: evals before launch, staged rollout, monitoring after (BJ’s Restaurants).
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering, personally accountable for every engagement. We’ll tell you plainly when a conversational interface is the wrong answer for your problem — which a vendor paid to ship one won’t.
Responsible AI is the founding charter. For an assistant speaking in your brand’s voice to your customers, governance — what it may say, when it must escalate, how it’s audited — is the product, not an afterthought.
Engine-agnostic. We benchmark OpenAI, Claude, and Gemini on your actual conversations and route to whichever wins. No partnership steers the recommendation.
Founder-led, one accountable lead. No account managers, no handoffs — the person who scopes it answers for it.
Built to transfer. Prompts, evals, and code are assigned to you; your team is trained to run and extend the assistant when we step back.
Patient-engagement and intake assistants inside HIPAA-compliant architectures, every answer grounded and logged. Healthcare software →
Support and servicing assistants where every response carries an audit trail and conservative, sourced answers. Fintech software →
Shopping and post-purchase assistants answering from live catalog and order data, deflection measured weekly.
What teams want to know before they put an assistant in front of customers.
A builder gives you decision trees; we build an assistant that reasons over your actual data and acts through your actual systems. The difference shows on the questions you didn’t script for — a builder fails, a grounded assistant answers or escalates. The engineering that makes that reliable (retrieval, tool use, evals, escalation) is the work; the chat window is the easy 5%.
Grounding plus measurement. The assistant answers only from your approved sources, every answer can cite where it came from, and we measure the hallucination rate against a golden set built from your real conversations before launch — then monitor it after. Where confidence is low, it escalates to a person rather than guessing.
Most often tier-1 customer support, IT service desk, HR self-service, sales qualification, and post-purchase operations — anywhere a high volume of repetitive requests is answered from data you already hold. We scope the use case first and decline the ones where a conversational interface isn’t the right tool.
Whichever wins your evaluation. We benchmark the candidates on your real conversations during design and route accordingly — and because the system sits behind a model abstraction, switching later is a config change, not a rebuild. See our LLM development services for how we work across all three.
The assistant runs in your own cloud tenant under your access controls; integrations use scoped, permissioned tool calls; and every engagement starts with an NDA and a security review. Business API traffic to the major providers isn’t used to train their models by default, and we document every data path so your team verifies rather than trusts.
You do — completely. Prompts, evaluation suites, and code transfer under full work-for-hire IP assignment signed at kickoff, and your team is trained to operate and extend it. Keep us on a reduced retainer or take the keys; the engagement is built around the handover.
Most assistants reach production in 4–8 weeks under a fixed-scope engagement with one accountable lead. Build cost depends on scope — our AI development cost guide gives real ranges — and run cost is token economics we model before building, so the first invoice is a forecast you’ve already seen.
Thirty minutes · No pitch deck
Bring the use case — we’ll tell you honestly whether a conversational interface fits it, what it takes to build, and what it costs to run.