AI & automation inside Salesforce: with Agentforce, Einstein and Flow
We implement Salesforce's native AI and automation — agents with Agentforce, predictive and generative AI with Einstein, reusable prompts with Prompt Builder, processes with Flow and unified data with Data Cloud — so your sales and service teams can summarize cases, reply assisted, prioritize leads and run the next best action without leaving the platform.
Salesforce AI isn't a loose LLM you bolt on from outside: it's a set of products that live inside the platform and feed on your CRM data.
Agentforce lets you deploy agents that handle work and execute actions on your objects (accounts, cases, opportunities). Einstein adds a predictive layer (lead scoring, opportunity insights) and a generative one (summaries, replies, assisted emails). Prompt Builder lets you create reusable prompt templates wired to real fields and records. Flow orchestrates sales and service process automation.
The code and configuration are 100% yours from day one.
Six operational reasons, zero adjectives
The code is yours from day one
Repos in your name, documented CI/CD and zero vendor lock-in. If you leave tomorrow, you take it all, running.
WhatsApp API with an official provider
We are a Meta Tech Provider: your WhatsApp Business API line with no middlemen, and chatbots wired to your ERP.
Sprint delivery, CMMI 2 processes
A working demo every two weeks and measurable progress. No "it’s 80% done" without something you can click.
AI applied to your operation
LLM agents, RAG over your data and process automation — the same practice we use to run iTech itself.
Real nearshore: Texas + Monterrey
Legal entity in the U.S. (iTech Corp, Texas), contracts under U.S. law, same CST time zone and USMCA.
ERP with CFDI 4.0 invoicing
We implement Odoo with integrated SAT stamping (PAC), client portal and reconciliation — a full operation, not just software.
When you need it
What's included
Prioritized AI use cases
Before touching any configuration, we define what you want AI for and where it adds real value: case summaries, assisted service replies, lead scoring, opportunity insights, next-best-action in sales, or process automation. We come out with an honest, prioritized scope — not "let's turn Einstein on and see what happens".
Agentforce & Einstein configuration
We implement the products your case calls for: Agentforce agents with bounded actions on your objects, Einstein for lead scoring and opportunity insights, and the generative features (summaries, replies, assisted emails) across Sales and Service Cloud. Each capability is configured with a clear scope, not the whole suite by default.
Prompt Builder templates
We create reusable prompt templates wired to your real fields and records (not loose prompts pasted by hand), so generative summaries, emails and replies use the right context for each account, case or opportunity and keep your business's tone and policy.
Grounding with your data (Data Cloud)
We ground the AI on your real data: we unify customer information with Data Cloud so Agentforce and Einstein's generative features answer from your CRM and trusted sources, not from the model's generic knowledge. It's the difference between AI that knows your customer and AI that guesses.
Process automation with Flow
We build and order sales and service process automation with Flow Builder (screen flows, subflows, invocable actions, scheduled flows): lead assignment, case escalation, approvals, field updates. We migrate legacy automations and leave a governed execution order, with no loops or duplicates.
Guardrails & evaluation
We bound what agents and generative replies can and cannot do: restriction to your business domain and policy, escalation to a human when there's no certainty, and review before sending. We evaluate quality with real and edge cases before and after go-live, using the platform's native observability — so it's trustworthy in front of customers.
How we work
1Audit & use cases
We map your existing automations (workflow rules, process builders, flows), identify conflicts, redundancies and loops, and spot where Salesforce AI adds real value. It's the same approach as our Salesforce assessment: an honest scope before committing budget.
2Design with governance
We define the automation and AI architecture with proper governance: execution order, naming conventions, which Einstein or Agentforce capability solves each case, what data must be unified in Data Cloud for grounding, and which guardrails apply. Documented, so it doesn't become a black box.
3Implementation
We build the flows, configure Einstein and Agentforce, create the Prompt Builder templates, connect grounding with Data Cloud and migrate legacy automations to Flow. We work on your org with CMMI Level 2-aligned processes.
4Evaluation & guardrails
Before go-live we test the agents and generative replies with real and edge cases, tune the guardrails and human escalation, and validate that the automation doesn't create loops or duplicate notifications in production.
5Optimization & adoption
After launch we monitor adoption, tune the predictive models and templates with real behavior, and expand to new processes based on the value each one delivers — instead of switching on features nobody uses.
Tech stack
The tools and platforms we build it with — chosen for your problem, not for hype.
Frequently asked questions
Can't find your question? Talk to an engineer — no sales script.
Contact us →Agentforce or an external LLM? When does each make sense?
It depends on where the case lives. Agentforce fits when the AI has to operate inside Salesforce, on your objects and CRM data, with actions and permissions already governed by the platform: case summaries, Service Cloud replies, agents that update opportunities. An external LLM makes sense when the case lives outside Salesforce or needs to integrate with many systems beyond the ecosystem. In this mini-site we focus on what's native to Salesforce; if your case is off-platform, we settle it in the assessment and tell you honestly which path fits — without pushing you toward whatever suits us best.
Do we need additional licenses or add-ons for Einstein and Agentforce?
Partly, yes. Some Einstein features are included in Enterprise editions or higher, while other capabilities — and Agentforce — require add-ons or a usage-based consumption model that Salesforce defines. We don't invent prices: we tell you exactly which capability your use case needs and help you validate the licensing cost directly with Salesforce or your account executive, so there are no surprises. We're partners in the ecosystem, not Salesforce; licensing is provided by the vendor.
What about the privacy of my data when using Salesforce AI?
Salesforce's generative AI is designed to run under its trust layer (the Einstein Trust Layer), which applies zero-retention policies for your prompts and masks sensitive data sent to the model, keeping your information within the platform's boundaries. Grounding with Data Cloud keeps your knowledge under your control in your own org. We configure the permissions, the scope of data sent and the guardrails according to your policy; during the assessment we turn this into concrete governance for your case.
How do you stop an agent or generative replies from hallucinating?
With several layers, all inside the platform. First, grounding: with Data Cloud and Prompt Builder, answers are built from your real CRM data and templates wired to fields, not the model's generic knowledge. Second, guardrails: we bound the domain and policy, configure escalation to a human when there's no certainty, and leave generative replies for review before sending in sensitive cases. Third, evaluation: we test with real and edge cases before and after go-live. We don't promise zero errors — no honest vendor does — but we do deliver a bounded, traceable system that's safe to put in front of customers.
How long does Einstein take to give reliable predictions, like lead scoring?
Einstein learns from your history: for useful lead scoring it needs a reasonable volume of records with historical conversion outcomes, and it typically requires a few weeks of initial training before producing predictions worth trusting. Exact timelines depend on the quality and volume of your data. We don't promise magic numbers or made-up accuracy: in the assessment we review your real history and tell you what's achievable and on what horizon.
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