AI applications that make it to production, not just the demo.
ProSer Insights designs, builds, and operates AI applications — conversational voice agents, LLM-powered assistants, document intelligence, and predictive features inside existing products. We run ProSer VoiceAI in production and ship TimeFlow HR, so we build for the realities a demo skips: guardrails, human handoff, cost per call, consent, and an audit trail.
Built for teams that need AI working inside a real process, on real data.
Most AI projects stall between the prototype and the first live user — at the integration, the edge cases, or the moment finance asks what it costs per transaction. We work with three kinds of teams.
Product companies adding AI features
who want copilots, search, summarization, or voice inside an existing product without rebuilding it. We design the feature, the model layer, and the evaluation harness, and ship it in your stack.
Enterprises and agencies automating a process
with inbound calls, intake forms, documents, or case queues that people handle today. We automate the routine share and design the handoff for everything else.
Founders who need an AI engineering team
from architecture through launch. We act as your applied-AI and full-stack team until you hire one, then hand over code, evals, and runbooks.
From the first use case to a system you can operate.
Two groups of services. Each ends with a working artifact your team owns, not a slide.
Build
Voice & conversational agents
Inbound and outbound voice agents for appointment booking, support triage, reminders, and intake — with telephony, calendar, EHR/CRM integration, and a designed handoff to humans. Built on the same platform that runs ProSer VoiceAI.
Deliverable
Live agent on your number with call logs, transcripts, and analytics; handoff and escalation rules documented.
LLM assistants & copilots
Retrieval-grounded assistants over your documents, tickets, and data; in-product copilots that draft, summarize, classify, and recommend. Citations, permissions, and prompt versioning from day one.
Deliverable
Assistant deployed in your app or Slack/Teams with an evaluation set and accuracy report.
Document intelligence
Extraction, classification, and validation for contracts, invoices, forms, claims, and RFPs — combining OCR, LLMs, and rule checks so structured data lands in your system with a confidence score and a review queue.
Deliverable
Processing pipeline with per-field accuracy, exception queue UI, and export to your system of record.
Predictive & ML features
Forecasting, scoring, matching, and anomaly detection embedded where decisions are made — a recruiter's queue, an operations dashboard, a scheduling screen — not a notebook.
Deliverable
Trained model behind an API or in-product feature, with monitoring for drift and a retraining plan.
Operate
Evaluation & guardrails
Golden test sets, automated evals on every prompt or model change, red-team scenarios, content and scope guardrails, and a clear definition of when the AI must stop and hand over.
Deliverable
Eval harness in CI with pass/fail thresholds; guardrail policy and test evidence.
Integration & data plumbing
Telephony (SIP/Twilio), calendars, EHR/CRM/ERP, ticketing, payment, and SMS — plus the pipelines that keep the AI's knowledge current and permissioned.
Deliverable
Integration inventory, connectors in code, and a data-refresh schedule with ownership.
LLMOps & cost control
Model routing, caching, token budgets, latency targets, and per-transaction cost dashboards so the unit economics are known before scale, not after.
Deliverable
Cost-per-call/document report and routing configuration with fallbacks.
Managed operation & tuning
We run the application after launch: monitoring, conversation review, prompt and model updates, incident response, and a monthly quality report — until your team takes over, or indefinitely.
Deliverable
Monthly quality and cost report; SLA-backed operations with named engineer.
Engineers who run an AI product in production. Not a lab.
- 1
We operate our own AI product.
ProSer VoiceAI handles live appointment and call-center traffic for real callers. TimeFlow HR runs append-only audit trails in production. The patterns we bring to your project — handoff design, consent capture, cost monitoring — come from operating these, not from reading about them.
- 2
One full-stack team, not just a model team.
React, NestJS, PostgreSQL, telephony, and cloud infrastructure alongside the AI layer. The people who write the prompt also build the UI it lives in and the integration it depends on.
- 3
Governance is part of the build.
Call-recording consent, data retention and residency, PII handling, role-based access, audit trails, and human override are designed in — so the application passes a security questionnaire, a HIPAA review, or a public-sector evaluation.
- 4
US and India delivery, one accountable lead.
A US-based lead owns scope and outcomes; the India engineering team gives you round-the-clock build capacity at a cost that lets a pilot become a program.
Working software before you commit.
- ProSer VoiceAI — conversational voice agent for appointment booking, support, and call-center triage with human handoff. Product ↗
- Document intelligence — extraction and validation pipelines for forms, contracts, and solicitations with a review queue.
- Case management, consent management, ticketing, event management — delivered platforms where AI features are added as copilots, search, and automation.
- Model-agnostic — OpenAI, Anthropic, Google, and open-weight models via a routing layer; deployed on your cloud or ours.
From use case to live pilot in weeks, then to a system you own.
The same team runs the pilot and the production build, so what is learned with the first fifty users is in the design of the next five thousand.
Pilot
Week 0–1
Discovery & use-case selection
Map the process, pick the use case with the clearest value and lowest risk, define success metrics.
Week 1–2
Data & integration audit
What data exists, where it lives, who may see it, and which systems the AI must talk to.
Week 2–4
Prototype
Working prototype on real (or realistic) data; first evaluation set built with your subject-matter experts.
Week 4–8
Live pilot
Limited real users or a share of live traffic, with handoff and monitoring in place.
Production
Gate
Evaluate
Accuracy, containment, latency, cost per transaction, and user feedback against the agreed metrics.
Sprints
Harden
Guardrails, edge cases, security review, load testing, and the remaining integrations.
Launch
Roll out
Phased rollout with rollback, training for operators, and the review queue staffed.
Ongoing
Operate & tune
Conversation and output review, prompt and model updates, monthly quality and cost reporting.
Priced for the stage you are at.
Fixed-scope pilot
A defined use case, an agreed success metric, and a live pilot in 6–8 weeks for a fixed fee.
Best for: validating one use case before a bigger commitment.
Scope a pilotRetained AI product team
A dedicated cross-functional team — AI engineer, full-stack developers, QA — on a monthly retainer, with a roadmap you control and a named lead.
Best for: companies building AI into a product or across several processes.
Talk about a teamStaff augmentation
AI/ML engineers, prompt and evaluation engineers, and full-stack developers placed with your team, onshore or offshore, through our staffing practice.
Best for: teams with a plan who need hands.
Request profilesWhat the work looks like.
Healthcare provider · Inbound calls
Voice agent for appointment booking and reschedules
Deployed ProSer VoiceAI on the clinic's line with calendar and patient-record integration and a warm transfer to front-desk staff for anything out of scope.
Professional services · Document intake
Contract and RFP intelligence pipeline
Built extraction and clause-classification for incoming solicitations and agreements, feeding a review queue and the proposal team's compliance matrix.
Staffing platform · Recruiter productivity
AI screening and matching inside the recruiting app
Added candidate-to-role matching, summary generation, and browser push notifications for recruiters into an existing React/NestJS platform.
Questions we get before an AI project.
Which models and vendors do you use?+
Whichever fits the task, cost, and data constraints — commercial APIs (OpenAI, Anthropic, Google) or open-weight models hosted in your cloud. We build behind a routing layer so you can switch models without rebuilding the application.
Is our data used to train anything?+
No. We use models under terms that exclude training on your data, keep your data in your tenancy or ours under contract, and document retention and deletion. For regulated data we can deploy models inside your VPC.
What happens when the AI is wrong?+
We design for it: confidence thresholds, human review queues, explicit handoff to a person, and audit logs of every decision. The evaluation harness measures how often it is wrong before and after every change.
What does it cost to run?+
We report cost per call, per document, or per request from the pilot onward and tune routing and caching against it. You will know the unit economics before you scale.
Can it integrate with our phone system, EHR, or CRM?+
Yes — SIP and cloud telephony, major calendars, EHR/CRM/ERP systems via API, and legacy systems via secure adapters. The integration audit in week one tells you exactly what is needed.
Do you build on our cloud?+
Your AWS, Azure, or GCP account, our managed hosting, or a hardened VPS — your choice. Code, prompts, evals, and infrastructure definitions are yours either way.
Related services and products
Have a process that should be automated?
Tell us about the calls, documents, or decisions you want handled. Within two business days you will have a use-case assessment, an integration checklist, and a fixed pilot quote.