AI, implemented into your operations
Not a chatbot you are left to configure. Our forward-deployed engineers implement the right model into your processes, with guardrails for regulated work, and stay until it delivers.
The toolkit
We are not a single-vendor shop. Each model earns its place on a specific task, and your engineer chooses against your real workload.
Nuanced reasoning, careful drafting, and customer conversations that have to stay on-script. Strong where the cost of a wrong answer is high.
Fast, general-purpose reasoning for classification, extraction, and summarisation across documents, email, and live voice.
Domain-specific intent classification, entity extraction, and quality scoring. We build these where general LLMs are too slow or too expensive at volume.
No vendor lock-in
Every model sits behind a provider abstraction. When a provider has a bad hour, raises prices, or deprecates the version you rely on, that is a config change for us, not a forced migration for you. Switchboard routes language-model calls across OpenAI and Anthropic with automatic failover, so a provider outage does not take your contact centre down with it.
Where custom models earn their place
For high-volume, domain-specific work, a model we train for the task is faster, cheaper, and more accurate than a general-purpose one. We use the big models where they shine and build our own where they do not.
Voice cannot wait for a slow general model. For high-frequency, narrow tasks, a smaller purpose-trained model answers in a fraction of the time.
Running a general-purpose LLM on every one of millions of cases is expensive. A custom model handles the repetitive classification, and the big model is reserved for the hard calls.
Trained on the language of your sector (claims, diagnostics, possession proceedings), a focused model beats a generalist on the task that actually matters to you.
A model on its own changes nothing. The value is in wiring it into your real workflows: tool use that creates cases and updates records, guardrails that keep it on-script for regulated work, integrations to your other systems, and clean handoff to a human when it reaches its limit.
That implementation is the job of a forward-deployed engineer. They build it with your team, against your data, until it runs.
Production AI taking real first-notification-of-loss calls for insurers. Not a demo, but handling live claims, creating cases, and updating records mid-conversation.
OpenAI-powered classification of automotive diagnostic reports, identifying the OEM system type and extracting the key data automatically.
When claim volumes spike during a weather event, multi-LLM failover keeps intake running: Switchboard moves mid-call across providers without dropping the conversation.
Straight talk
If the underlying data is fragmented or wrong, a model just produces confident nonsense faster. We integrate and standardise the data first. That is usually the unglamorous part of the work.
Plenty of problems are better solved by a deterministic workflow than by an LLM. We will tell you when AI is the wrong tool rather than bolt it on to justify the word.
Because providers sit behind an abstraction, moving from one to another is a config change. The conversation logic, tool use, and your case data do not move.
Questions we get asked
Yes. The architecture is provider-agnostic. If you have a contractual or compliance reason to prefer a specific provider (or your own fine-tuned model), we route to it. The default is multi-provider with automatic failover; pinning to one is an option, not the norm.
Per task. High-stakes reasoning and careful customer conversations lean towards Claude; fast general classification and extraction lean towards OpenAI; high-volume, narrow, latency-sensitive work gets a custom model. Your forward-deployed engineer makes the call against your real workload, not a benchmark.
Your case data, audit logs, and conversation history stay in UK data centres regardless of which AI provider handles a given request. Provider failover happens inside the SwiftCase platform, not by shipping your data somewhere new.
Switchboard fails over to the next provider within the timeout window of a single call: seconds, not minutes. Conversation context and tool-use state carry over because they live in SwiftCase, so a mid-call failover does not restart the caller.
No. The models are one layer. The value is in implementing them into your workflows with tool use, guardrails, integrations, and human handoff, and in the custom models we train for the tasks general LLMs handle poorly. That implementation is what our engineers do.
We will look at your operation and tell you honestly which tasks suit Claude, which suit OpenAI, which need a custom model, and which need no AI at all.
