Custom AI development builds AI into your own applications and products, where an off-the-shelf assistant does not fit.
Architect-led · OpenAI Select Partner · Tested before every release
Custom AI application development for AI features that have to work in production.
We design and build AI into your applications and products: the data pipelines, retrieval, evaluation and monitoring around the model, so the feature stays accurate, fast and affordable after launch.
See the architecture
- 15+Years of team experience
- 20+Enterprise implementations
- 60 minFree custom AI development review, with a written plan
- 5.0/5Client satisfaction (CSAT)
Proven Success With
5.0/5 CSAT
New to custom AI development?
What is custom AI development?
Most of the work is around the model: data pipelines, retrieval, permissions, evaluation and the code that calls it.
LLMOps keeps it working after launch: versioned prompts, monitoring, cost tracking and a test set run on every change.
Where you are starting from
Starting an AI build, or rescuing one
An idea and a deadline
You know the feature you want but nothing is built yet.
First stepAn architecture and a thin working slice tested on real data.
A prototype that stalled
It worked in a demo, but answers drift and nobody trusts it.
First stepA test set, so we can measure what is wrong before changing it.
Live, but costly or unreliable
The feature is in production with rising cost or complaints.
First stepMonitoring and tracing, then the fixes the data points to.
What we do
What does custom AI development include?
Eight workstreams, scoped to what your feature needs.
- ARC
AI architecture
Components, hosting, security and a model gateway for your stack.
- DAT
Data pipelines
Content extracted, cleaned, split and kept in sync.
- RAG
Retrieval
Search over your content with permissions and citations.
- APP
Application build
APIs, interfaces and integrations where users work.
- EVL
Evaluation
Test sets, graders and release gates.
- FTN
Fine-tuning
Only when prompts and retrieval fall short in tests.
- OPS
LLMOps and MLOps
Versioned prompts and models, CI/CD and rollbacks.
- MON
Deployment and monitoring
Quality, latency and spend tracked in production.
Architecture
What does a production AI application architecture include?
The parts around the model, and how they connect.
Your application
Model gateway
Models
Search index
Data pipeline
Your data sources
Identity and permissions
Evaluation
Monitoring
Application layer
Your app, its APIs and the identity check on every request.
Model layer
A gateway that routes and logs calls, so models can be swapped.
Data layer
Pipelines and a search index kept in sync with the source systems.
Operations
Evaluation and monitoring that catch problems before users do.
Fine-tuning
When is fine-tuning worth it?
We try the cheaper fixes first and measure each one. Fine-tuning comes in only when the tests say so.
Clear instructions
Prompts written and tested against real cases.
Retrieval
Your content added, so the model has the facts.
Examples and structure
Worked examples and structured output formats.
Another model
A different model or size tried on the same tests.
- Still short of the bar in tests?
Ship it
The test set passes. Release with monitoring.
Fine-tune
For a consistent format, style or narrow task at volume, with data to train on.
Rethink the task
Split the job, add a human step or change the scope.
Evaluation
How do you test an AI application before release?
A test set built from real cases runs in CI/CD, so a prompt or model change cannot quietly break the feature.
run-evals support-assistant --set release
- PASSAnswers policy questions from the right source
- PASSCites the passage it used
- PASSWithholds records the user may not see
- PASSReturns valid JSON for the order API
- HUMAN REVIEWTone on complaint replies
Built from real cases
Questions and expected answers come from your logs and experts.
Wired into CI/CD
The set runs on every prompt, model or code change.
People grade judgment calls
Automated graders for facts and format, people for tone and edge cases.
LLMOps
What do you monitor after an AI application goes live?
The views we set up at release, so problems show up before users report them.
AI application health
User feedback
Ratings, with the conversation behind each one.
Latency
Response time per step, so slow calls are found.
Spend
Model usage cost per feature and user group.
Errors and refusals
Failed calls, timeouts and blocked requests.
Drift
New kinds of questions the test set does not cover yet.
How it runs
How does a custom AI development project run?
Six phases, run as parallel workstreams. Your written proposal sets the dates.
Workstream
- 01Discover
- 02Design
- 03Build
- 04Evaluate
- 05Release
- 06Run
Use case
Data
Application
Evaluation
Operations
Cost and timing
What it costs and how long it takes
The review is free
A 60-minute architecture review of your AI application 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
Custom AI development, answered.
Straight answers about fine-tuning, LLMOps, hosting, costs and model changes.
Building AI features and applications on your own code and data: architecture, data pipelines, retrieval, evaluation, deployment and monitoring, where an off-the-shelf assistant does not fit.
Usually not at first. Prompts, retrieval and structured outputs solve most cases. We fine-tune when tests show a gap they cannot close and you have the data to train on.
The practices that keep language model applications reliable: versioned prompts and models, evaluation in CI/CD, monitoring of quality, latency and cost, and safe rollbacks.
MLOps covers training and serving your own models. LLMOps applies the same discipline to applications built on large language models, adding prompts, retrieval and output grading.
We work in your stack, commonly Python or TypeScript, on AWS, Microsoft Azure or Google Cloud, using the model APIs or open-weight models you approve.
Yes. We deploy to your cloud account, and use open-weight models on your infrastructure where data must not leave it.
Smaller models for simple steps, caching, shorter prompts, and spend tracked per feature so changes can be measured.
The model gateway and the test set make a switch measurable: run the tests on the new model, compare results, and change only when it passes.
OpenAI is a trademark of OpenAI. AWS is a trademark of Amazon.com, Inc. Microsoft Azure is a trademark of Microsoft. Google Cloud is a trademark of Google LLC. Python is a trademark of the Python Software Foundation. TypeScript is a trademark of Microsoft. Salesforce is a trademark 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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