Generative AI writes text, summaries, code and images from instructions, using large language models from vendors such as OpenAI, Anthropic and Google.
OpenAI Select Partner · Architect-led · Works across model vendors
Generative AI for business, grounded in your content and tested before people rely on it.
We put generative AI to work on your documents and records: answering questions with sources, pulling data from forms, summarizing long cases and drafting replies, with guardrails and evaluations built in.
See how RAG works
- 15+Years of team experience
- 20+Enterprise implementations
- 60 minFree generative AI review, with a written plan
- 5.0/5Client satisfaction (CSAT)
Proven Success With
5.0/5 CSAT
New to generative AI?
What is generative AI?
For business use it is grounded: retrieval-augmented generation (RAG) feeds the model passages from your own documents, so answers rest on your content and cite it.
Guardrails check what goes in and what comes out, and evaluations grade it on real questions before staff or customers rely on it.
When to start
When is generative AI worth starting?
Most generative AI projects begin with one of these problems.
Answers are hard to find
Staff search several systems and still ask a colleague.
Documents keyed by hand
Invoices, forms or claims are retyped into a system.
Long reading before decisions
Cases, contracts or reports take too long to get through.
The same replies written again
Teams draft similar emails and articles all day.
Staff using public AI tools
Company data is pasted into tools nobody approved.
Use cases and services
What can generative AI do for your business?
Five use cases we build most, and the three disciplines that make them dependable.
Knowledge search
Answers with citations from your content.
Document processing
Fields extracted into your systems.
Summarization
Cases, calls and reports cut short.
Drafting
Emails and articles for a person to edit.
Code assistance
Assistants set up with your standards.
Model selection
The right model for each use case.
Guardrails
Input and output checks.
Evaluation
Real questions graded on every change.
Retrieval-augmented generation
How does retrieval-augmented generation work?
A staff member asks a question. The answer comes from your documents, with the sources shown.
QuestionChat or search box
Input checkPrivate data and topic
Check accessWhat this user may see
Search your contentRelevant passages
Model writes the answerFrom the passages only
Answer with sourcesOutput check and citations
Search comes first
Your documents are split, indexed and searched, so the model sees only the relevant passages.
Access follows the user
Retrieval applies the permissions of the person asking.
Sources on every answer
Each answer links to the passages it used, so people can check it.
Model choice
Which generative AI model should you use?
We choose per use case on quality, cost, speed and data terms, and design so the model can be changed later.
- Model family
OpenAI
GPT models through the OpenAI API or Microsoft Foundry on Azure.
- Model family
Anthropic Claude
Through the Claude API, Amazon Bedrock, Google Cloud or Microsoft Foundry.
- Model family
Google Gemini
Through the Gemini API or Google Cloud, with text, image, audio and video input.
- Self-hosted
Open-weight models
Llama, Mistral, Gemma or gpt-oss on infrastructure you control, when data must stay in house.
- Routing
More than one model
A small, fast model for simple steps and a larger one for hard questions, behind one gateway.
Guardrails
What guardrails does generative AI need?
Checks before the model, after the model and after release.
- Preventive
Private data filter on input
Personal or confidential data sent to a model not approved for it.
- Preventive
Permission-aware retrieval
Users seeing documents they are not allowed to open.
- Preventive
Topic and instruction limits
The assistant going off topic or following instructions hidden in a document.
- Preventive
Output content filter
Unsafe, offensive or off-brand text reaching a customer.
- Detective
Grounding check
Answers stating things the sources do not support.
- Detective
Feedback and sampling
Quality slipping after a model, prompt or content change.
How it runs
How does a generative AI project run?
Six steps from use case to release. Your written proposal sets the scope and dates.
- Choose
- One use case and owner
- Success criteria agreed
- Collect
- Source content found
- Access rules mapped
- Ground
- Content indexed
- Retrieval tuned
- Build
- Prompts and guardrails
- Interface or integration
- Evaluate
- Real questions graded
- Fixes made
- Release
- Pilot group first
- Monitoring on
Cost and timing
What it costs and how long it takes
The review is free
A 60-minute architecture review of your generative AI setup, with a written plan you keep.
A fixed-scope proposal first
Before any work starts you get a written proposal: a fixed-scope project with milestones, or a monthly plan. You approve it, or you do not.
Timing follows scope
How long it takes depends on what is in scope. The proposal states the milestones, so you see the timeline before you commit.
Client stories
Client work we have delivered
Real engagements, described by industry. Each story covers the starting position, what we built and how we delivered it, end to end.
Communications, Media & TechnologyFour Production AI Systems for a Japanese Contact Centre OperatorCustom AI for a Japanese contact centre operator: recorded and live Japanese call transcription, 16-field emergency form extraction with human review, a bilingual knowledge base, and realtime operator assistance.Custom AI · Speech-to-text · Large language models · Vector searchRead the story 
Financial ServicesAI Deal Matching That Screens Companies Against Every Investor MandateSales Cloud, Agentforce, Data 360 and Prompt Builder for a US private equity advisory firm: mandates, AI first-pass ranking, deep scoring with rationale, and deals created from approved matches.Sales Cloud · Agentforce · Data 360 · Prompt BuilderRead the story 
Financial ServicesSelf-Hosted AI Research Infrastructure Behind a Salesforce Deal EngineSelf-hosted AI agent infrastructure for a US private equity advisory firm: n8n workflows, PostgreSQL, web research and Google Drive mandate documents, deployed with Coolify and writing results back to Salesforce.n8n · PostgreSQL · Coolify · FirecrawlRead the story Client reviews
What clients say about working with our architects
See how teams describe working with SynconAI, from first scope to production delivery.
Salesforce Select Partner30+ Salesforce certificationsDelivery from Delaware & Sydney, worldwide
Simply Outstanding
“SynconAI Consulting are Simply the Best in the Business When it comes to Salesforce implementation and AI-powered solutions, SynconAI are in a league of their own. Their expertise, dedication, and ability to deliver results that truly move the needle sets them apart from every other consulting partner we've worked with. What they achieved with our Sales Cloud, Service Cloud, and custom Agentforce agent has completely transformed how we operate streamlining our pipelines and our customer service, and automating tasks that used to consume hours of our team's time. They don't just implement technology they transform businesses. If you want the best, you work with their team.”
Sales CloudService CloudAgentforce
Jack BennettConsumer Goods & Retail · United States
“I had a very urgent deadline for our project reporting to be delivered and needed to build the module, dashboard and output reports in Salesforce. SynconAI were very responsive - they met with me online, responded to my emails quickly and took calls - whatever was needed to progress the work quickly. We are thrilled with the outcome and will continue to work with SynconAI in the future to continue to build our Salesforce capability and platform.”
Sales CloudService CloudNonprofit Cloud
Salesforce Managed ServicesAustraliaSalesforce Verified
“The team at SynconAI were fantastic, and promptly delivered on each project. They were able to guide us through every stage and made sure we were satisfied with the outcomes on multiple scopes of work.”
Sales CloudSlackFlowAgentforceMuleSoft
ManufacturingAustraliaSalesforce Verified
“They have been great and helping us transform out business by bringing what I conceptualize to life. Great at translating process/workflow needs into a solution. Been a pleasure work with and they're a critical part of our team and will be instrumental into bringing about my vision.”
Sales CloudFinancial Services CloudArchitectureAI
Financial ServicesUnited StatesSalesforce Verified
“Working with SynconAI was a seamless and highly professional experience from start to finish. They took the time to deeply understand our business processes before designing and implementing a Salesforce solution tailored specifically to our operational needs. The team demonstrated strong technical expertise across Salesforce configuration, automation, integrations, reporting, and user experience design.”
SalesforceExperience CloudCustom DevelopmentAutomationReporting
Salesforce ImplementationSalesforce Verified
Frequently asked questions
Generative AI, answered.
Straight answers about RAG, fine-tuning, model choice, data and accuracy.
Generative AI used on company work: answering questions from your documents, processing forms, summarizing and drafting, grounded in your data and checked before people rely on it.
RAG searches your own content for passages relevant to a question and gives them to the model, so the answer is based on your documents and can cite them.
Rarely as a first step. Retrieval keeps answers current and traceable. Fine-tuning helps with a fixed format or style at volume, and we test it against retrieval before recommending it.
OpenAI, Anthropic Claude, Google Gemini and open-weight models such as Llama, Mistral and Gemma. We choose per use case and design so the model can be changed later.
We use business API or cloud terms, review each vendor's data and retention terms with you, and use a self-hosted open-weight model where data must not leave your environment.
Answers are grounded in retrieved sources and cite them, a grounding check flags unsupported claims, and real questions are graded before release and on every change.
Yes. The assistant can sit in a web app, Microsoft Teams, Slack or Salesforce, depending on where your people already work.
It depends on the content and integrations involved. Your written proposal sets the scope, milestones and dates before anything starts.
OpenAI, GPT and gpt-oss are trademarks of OpenAI. Claude and Anthropic are trademarks of Anthropic, PBC. Gemini, Gemma and Google Cloud are trademarks of Google LLC. Llama is a trademark of Meta. Mistral is a trademark of Mistral AI. Microsoft Foundry, Azure and Microsoft Teams are trademarks of Microsoft. Amazon Bedrock is a trademark of Amazon.com, Inc. Salesforce and Slack are trademarks of Salesforce, Inc. SynconAI is an independent consulting firm; apart from its Salesforce and OpenAI partner status, it is not affiliated with or endorsed by these companies.
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