GAT Solutions

AI engineering · RAG · agents

Production AI engineering, RAG, and agent systems

Ground models in approved knowledge, connect them to Salesforce and the go-to-market (GTM) stack, give them tightly scoped tools, and add the evaluations and controls required to run the system in production.

Book a ConsultationSee the outcomes
  • 50+ Salesforce projects
  • 100+ integrations
  • 5.0 client rating
  • 6+ years Salesforce

A useful AI system is more than a prompt and a model. It needs reliable source data, an ingestion path, retrieval that finds the right context, tools that do only what they should, evaluation against real questions, and an operating plan for failures and change.

GAT engineers that full loop. We build retrieval-augmented generation systems across Salesforce records, knowledge bases, documents, support history, and product or revenue data; connect models through MCP and APIs; and design human approval around consequential actions.

The same team can handle the Salesforce objects and permissions, the cross-platform integrations, and the AI engineering. AI accelerates our delivery process, while senior developers and architects remain accountable for the architecture, security, validation, and production outcome.

AI engineering trace

One accountable consultant → governed gateways → metered outcomes

human operated
Claude

Accountable operator

Senior GAT consultant

GTM Systems EngineerAI EngineerArchitect
ChatGPT

AI drafts, retrieves, and executes tools; the consultant directs, reviews, and owns every write.

ApexGenius · Salesforce MCP
Custom MCP servers + APIsOAuth · tiered write gating

The client's systems stay the source of truth

CRMSalesforce org
ClinicalEHR records
VoiceFive9 telephony
Automationn8n
AnalyticsData warehouse

Metered, not claimed

47 orgs · 2,645 metered calls

ApexGenius MCP gateway in production

92,000+ chunks · 16,550 answers

RAG corpus serving governed answers

HIPAA-conscious translation

Gemini workflow inside a healthcare org

114 tools · tiered write gating

Custom MCP server — reads free, writes gated

59 built · 21 live

n8n workflows running client operations

Consultant runs every sessionGateways scope every writeOutcomes are metered, not claimed

Who this is for

Built for teams like yours

Teams with knowledge trapped across systems

The answer exists, but it is scattered across Salesforce, documents, tickets, and internal tools.

  • RAG over approved business knowledge and live system context
  • Source-aware answers with citations or traceable evidence
  • Access boundaries that follow the user and the source system

Salesforce teams moving beyond summaries

The assistant can draft text, but it cannot safely inspect the org or complete the next step.

  • MCP and API tools for records, metadata, and approved actions
  • Human review before high-impact writes or deployments
  • Evaluation against the actual sales, service, or admin workflow

Product and operations teams needing AI engineering depth

The use case is real, but the team does not have an AI engineer to take it from prototype to production.

  • Architecture and hands-on implementation in one engagement
  • Model, retrieval, orchestration, and integration decisions defended
  • Documentation and operating ownership after launch

Capabilities

What production AI engineering covers

RAG architecture

Design what knowledge enters the system, how it is segmented and indexed, which metadata matters, and how retrieval stays permission-aware and explainable.

Knowledge ingestion & retrieval

Build ingestion, normalization, chunking, embeddings, search, reranking, freshness, and source-linking pipelines across documents and operational systems.

Model & tool integration

Connect Claude, OpenAI models, Agentforce, or the model that fits to Salesforce, APIs, MCP tools, and deterministic workflow steps.

Agents, chatbots & workflow orchestration

Build customer and employee chatbots, sales and service assistants, and authenticated conversational apps with bounded tools, state, human handoff, retries, and exception handling.

Evaluations & observability

Create representative test sets and monitor retrieval quality, answer quality, tool calls, latency, cost, failure modes, and human overrides.

Security & production operations

Apply least-privilege access, sensitive-data controls, auditability, revocation, deployment discipline, runbooks, and named ownership.

GAT Solutions brought strong Salesforce and development depth, communicated clearly, delivered within the agreed timeframe, and earned the client's confidence for future work.

Salesforce delivery partnership · Verified client review

How we deliver

From operating problem to a system your team can run

  1. 01

    Prove the information path

    Trace the decision, source knowledge, user, permissions, and action before choosing models or infrastructure.

  2. 02

    Build and evaluate one complete loop

    Ship a vertical slice from retrieval through answer or action, tested against representative questions and failure cases.

  3. 03

    Harden the production system

    Add monitoring, approval, security, fallback behavior, documentation, and ownership before expanding scope.

Questions

Frequently asked questions

What does an AI engineering engagement deliver?
A working system, not only a strategy deck. Depending on the use case, that can include ingestion and retrieval pipelines, a vector or hybrid search layer, model and tool connections, agent orchestration, Salesforce integration, evaluation suites, monitoring, security controls, deployment, and documentation.
What is RAG, and when is it useful?
Retrieval-augmented generation finds relevant information from approved sources and gives that context to the model before it answers. It is useful when responses must reflect current company knowledge, customer history, policies, product documentation, or other information the base model does not reliably know.
Can a RAG system use Salesforce data and permissions?
Yes. The design can retrieve live Salesforce context at request time, index approved content outside Salesforce, or combine both. We define identity, sharing, field-level access, source permissions, and sensitive-data handling as part of the retrieval architecture.
Do you build custom chatbots and conversational applications?
Yes. GAT builds customer and employee chatbots, sales and service assistants, and authenticated conversations such as patient-to-nurse chat. The system can use Salesforce context, route to the right team, preserve conversation history, invoke approved tools, and hand off to a person. In clinical settings, AI supports routing and communication; it does not replace clinical judgment.
Which models, vector databases, and frameworks do you use?
The choice follows the workload, security constraints, existing cloud, latency, cost, and the level of operational control your team needs. GAT can work across Claude, OpenAI models, Agentforce, MCP, managed retrieval services, vector databases, and custom orchestration without forcing the use case into one vendor's stack.
How do you know the AI system is accurate enough to launch?
We define representative questions, expected sources, acceptable answers, failure cases, and tool outcomes before launch. Retrieval and response quality are evaluated separately, consequential actions stay reviewable, and production monitoring tracks the cases the system does not handle well.
When should we use ApexGenius instead of a custom AI build?
Use ApexGenius when the core need is a hosted, flat-subscription connector that gives Claude or ChatGPT governed access to Salesforce records and metadata. Choose an AI engineering project when you need custom knowledge ingestion, RAG, cross-system orchestration, specialized tools, evaluations, or a user experience built around your workflow. Current product pricing lives on the ApexGenius site.

Initial systems consultation

Build the AI system around a real operating decision.

Book a ConsultationStart with the Audit
  • You leave with
  • A focused consultation with a senior systems consultant
  • A current-state fit and architecture read
  • A recommended path — implementation, ongoing ownership, or product