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description=HyScaler makes AI work in your operations: data readiness, agents integrated into the systems you already run, and managed operations after launch. We also build complete products and platforms end to end.;
keywords=AI transformation,agentic AI,AI agents in production,AI consulting,software development,product engineering;

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Text of the page (random words):
source and self hostable taskosaur com hycultivate ai native cultivation erp from seed to sale hycultivate com lexross legal ai in your own cloud your knowledge your control lexross ai verbatium ai meeting notes on the record off the cloud verbatium com carenuance ai powered telemedicine healthcare access from anywhere carenuance com cranberryware one business hub for attendance performance and crm cranberryware com about about us our people awards certifications careers careers life at hyscaler our journey partners partners contact us thirty minutes with an engineer no pitch deck have a project in mind send a business enquiry ai transformation end to end engineering all services products case studies insights company book a call thirty minutes with an engineer no pitch deck the next ninety days day 90 day 00 day 00 ai transformation consulting end to end engineering we cross out the hype we build what works talk to an expert see how we build what works team of 150 10 years iso 9001 2015 cmmi l5 certified experts microsoft aws google oracle servicenow salesforce live in 90 days human sign off active your systems your data your cloud deployed inside your aws gcp or azure account human sign off a person approves anything consequential evals first written before the agent run against your data live in 90 days one workflow to production on a schedule run after launch monitoring guardrails and cost control you own it code models and evals land in your repositories day 00 day 00 enterprise ai strategy you do not need an ai strategy deck you need one workflow in production the usual way a 12 month roadmap deck beautifully argued never shipped the calendar moves the operation does not the usual way a platform purchase before the first win license first use case later the tool arrives before anyone knows what it is for the usual way pilot after pilot each demo impresses the room monday stays the same the usual way ai everywhere at once forty use cases on a slide none of them owned none of them live the working way one workflow that matters picked in week one inside your operations with the requirement in writing live in your systems in 90 days wired into the erp crm and queues you already run your cloud your data a number that moves one metric agreed up front monitored after launch that is what production means then the next one the second workflow reuses the data work the evals and the guardrails of the first one becomes two two becomes the operation that is the whole strategy day 01 day 01 ai transformation consulting the first five days are already scheduled week one runs inside your operations not a conference room five days each with a job ending in writing 01 mon sit with your team we watch the real work run end to end as it actually happens no slide decks 02 tue map every workflow everything the work travels through on one list in plain language 03 wed cross most of it out workflows that need a rules engine a template or a process change get named as exactly that 04 thu pressure test the one the survivor shows its data its metric and where a person signs off 05 fri commit in writing the requirement the tool choices and the 90 day plan for the workflow that earns it an example week yours will count differently 12 mapped 11 cut 1 kept by friday you know where ai belongs and where it does not day 01 90 days 01 90 ai implementation 90 days crawl walk run not a roadmap deck an implementation schedule one ai workflow goes from idea to production in 90 days crawl walk run we call it cwr90 drag the day or use arrow keys run day 90 crawl 01 30 walk 31 60 run 61 90 phase 01 days 01 30 crawl we map your operations and mark where ai belongs and where it doesn t you get the requirement the tool choices and the plan in writing use case shortlist scored d08 data readiness audit d15 tool selection with reasons d23 the plan in writing d30 this is the data readiness work phase 02 days 31 60 walk we build the one workflow that matters low stakes real data your systems your people watching it work working agent in your systems d38 evals written before the agent d45 human checkpoints designed in d53 weekly demos on real data d60 this is the agentic ai work phase 03 days 61 90 run it goes to production and we run it with you monitoring guardrails and a number that moves live in production d68 monitoring and guardrails d75 cost per run tracked d83 the day 91 runbook d90 this is the agentops work if day 30 shows ai does not belong where you thought we say so and the plan changes that is the point of crawling first day 01 is a thirty minute scoping call book day 01 day 90 day 90 ai agents in production shipped still running not projections engagement records agents in production industrial 2025 agentic ai industrial solutions document retrieval 10 minutes to under 30 seconds read the case study ai lending platform live fintech 2025 accelerating lending with aws 200 loan programs one platform read the case study end to end build live healthcare 2023 ehr integration for senior living care live across 200 care sites in 12 states read the case study browse all case studies 150 team members 10 years shipping software 50 clients worldwide iso 9001 2015 cmmi l5 and when ai is only half the job the same bench builds the whole product web mobile data cloud devices you do not need a second vendor to finish the job software development product engineering mobile app development data engineering devops ui ux product design iot development blockchain development explore all services under the hood one accountable bench not six subcontractors one team one written plan one owner the engine s capabilities share the same evals the same guardrails and the same delivery discipline so a decision made in week one propagates through everything that ships 06 your operations erp crm queues floor 05 agent layer orchestration integration 04 evals guardrails tests audit kill switch 03 data layer readiness pipelines lineage 02 model gateway anthropic openai open weights 01 your cloud aws gcp azure your vpc what we do the capability behind the promise six disciplines one bench this is the standing muscle every engagement draws on whether it is a 90 day agent or a complete platform built end to end foundations data readiness engineering build agentic applied ai systems agent integration engineering operate agentops managed operations govern guardrails human sign off deliver end to end product engineering one bench six disciplines since 2010 foundations data readiness engineering agents inherit every gap in your data schemas quality lineage and access get fixed before anything is built on them schema quality lineage access intake schema diff vendor_id not null issued_at iso 8601 line_items unnested notes_freetext unparsed fixed first so the agent inherits none of it foundations before agents data engineering build agentic applied ai one workflow that matters with evals written before the agent and human checkpoints where they count evals rag document ai predictive evals intake spec assert extraction matches_ground_truth assert human signed_off assert pii redacted assert cost within_budget written before the agent run every build evals before the agent applied ai systems agent integration engineering wired into the systems you already run with the access controls your auditors expect erp crm queues ehr mcp mcp servers toml erp scope read orders crm scope write notes queue scope claim tickets docs scope read archive least privilege per system reviewable retrofit first applied ai operate agentops managed operations production is the starting line we run what we ship on a managed retainer monitoring evals in prod cost control ops policy yaml on eval_fail rollback on cost_spike throttle on drift retrain on timeout human agreed before launch not after an incident we run what we ship agentops govern guardrails human sign off nothing consequential ships without a named person approving it role scope audit trail kill switch policy signoff rules approve requires named_human scope requires role_grant action writes audit_trail agent exposes kill_switch from day one not bolted on later every consequential action audited agentops deliver end to end product engineering when the workflow needs a product around it the same bench builds it ten years of complete platforms web mobile data cloud devices bench stack txt build web mobile product data pipelines warehouse run cloud devops support edge devices iot vision no second vendor to finish the job ten years of shipped platforms all services the loadout the stack this bench actually works in named not implied every line below has shipped in a client engagement and the list is reviewed rather than generated 76 named 6 domains reviewed not generated 01 ai ml 20 models orchestration evals vectors anthropic claude openai mistral ai deepseek meta llama hugging face langchain langgraph llamaindex mcp ollama pytorch tensorflow scikit learn mlflow jupyter nvidia qdrant pinecone 02 languages backends 12 the services behind the agent python typescript javascript node js go rust fastapi django express laravel ruby on rails wordpress 03 web product 8 what your users actually touch react next js astro vue js graphql tailwind css vite figma 04 mobile 5 shipped to both stores flutter dart kotlin swift android 05 data 15 the foundations agents depend on postgresql mysql mongodb redis elastic stack snowflake databricks bigquery apache kafka apache airflow apache spark rabbitmq salesforce oracle sap 06 cloud devops 16 where it runs and how it stays up aws azure google cloud kubernetes docker terraform ansible nginx firebase supabase prometheus grafana git github gitlab n8n reviewed for accuracy not for length if something you depend on is missing ask the answer is usually yes or an honest no integrations your systems one expert in the loop a hyscaler expert sits between the tools you already run and the surfaces where your teams work wiring the agents in and moving governed data both ways pick a workflow and follow one request end to end finance ops customer support healthcare supply chain it service desk data reporting trace invoice 4471 replay finance ops your systems governed boundary our side 01 sharepoint invoice received scope read documents 02 agent fields extracted evals passed before release 03 oracle erp po matched scope read orders 04 hyscaler expertise approved named sign off nothing consequential moves without it 05 oracle erp invoice posted scope write invoices 06 audit trail entry written who approved what changed when trace case 8823 replay customer support your systems governed boundary our side 01 salesforce case created scope read cases 02 agent intent classified evals passed before release 03 retrieval policy cited sources returned with the answer 04 hyscaler expertise approved named sign off the reply is a person s call 05 salesforce reply posted scope write cases 06 audit trail entry written who approved what changed when trace referral 2190 replay healthcare your systems governed boundary our side 01 ehr fhir referral received scope read patients 02 agent codes suggested evals passed before release 03 redaction pii masked nothing leaves your tenancy 04 your clinician approved a clinical call is never the agent s to make 05 ehr fhir record updated scope write encounters 06 audit trail entry written who approved what changed when trace order 5567 replay supply chain your systems governed boundary our side 01 sap exception flagged scope read orders 02 agent cause diagnosed evals passed before release 03 postgresql history checked scope read shipments 04 your ops lead approved the fix named sign off nothing consequential moves without it 05 sap order corrected scope write orders 06 audit trail entry written who approved what changed when trace incident 3312 replay it service desk your systems governed boundary our side 01 servicenow incident raised scope read incidents 02 agent logs correlated evals passed before release 03 elastic stack traces searched scope read logs 04 your on call engineer approved the runbook step a production change is never the agent s to make 05 servicenow incident updated scope write incidents 06 audit trail entry written who approved what changed when trace metric 0914 replay data reporting your systems governed boundary our side 01 apache kafka event consumed scope read streams 02 agent anomaly flagged evals passed before release 03 snowflake history queried scope read marts 04 your data lead approved the restatement figures are not republished on an agent s word 05 dashboards bi figures republished scope write dashboards 06 audit trail entry written who approved what changed when illustrative traces every write back lands in the log systems we read and write salesforce servicenow oracle erp sap sharepoint ehr fhir postgresql mysql mongodb snowflake databricks bigquery apache kafka elastic stack surfaces work lands in ticket queues approval flows dashboards bi email documents chat alerts audit reports mobile apps webhooks apis appendix reference a1 questions before you book the call the questions engineers get asked in the first thirty minutes answered up front 01 what does a typical engagement look like ninety days in three phases crawl scoping data and cloud foundations walk the agent or product built and integrated into your systems and run it goes live and we operate it with you fixed scope weekly demos against real data and written success criteria you sign before we build 02 what if ai is the wrong answer for our problem then we say so in writing before you spend some problems are better solved with a workflow change or conventional software shipping the wrong thing helps nobody and our engineers would rather keep your trust than your retainer 03 do you work inside our existing systems or replace them retrofit first agents are integrated into the erp crm and ticket queues you already run with the access controls your auditors expect a decade of integration work across microsoft aws google oracle servicenow and salesforce is exactly the muscle we use 04 how is cwr90 priced fixed price quoted at the scoping call once we have seen the use case and the systems involved delivery runs from bhubaneswar with a santa clara presence which is why the number does not look like a strategy firm s 05 who owns what we build you do code models prompts and evaluation suites are delivered into your cloud and your repositories if we ever part ways everything keeps running without us 06 do you only do ai work no we have built products and platforms end to end for ten years web mobile data cloud devices the ai work sits on that foundation and the same bench builds whatever surrounds the agent so you do not need a second vendor to finish the job 07 how do you handle our data it stays in your cloud inside your aws gcp or azure account behind your access controls nothing is used to train anything outside your walls and every c...