Services
Production AI Pipelines
AI features that hold up outside the demo
We build AI into products people pay for — voice agents that answer real calls, document intake that feeds real records, in-app assistants with genuine context, and generation pipelines running at volume. The prompt is never the hard part. Latency, cost, failure handling and vendor portability are.
The gap between an impressive prototype and a production feature is mostly unglamorous engineering: what happens on a timeout, how a bad extraction gets caught before it corrupts a record, what a provider price change does to your margin, and how quickly you can leave a vendor that stops working for you.
You probably need this if
- The demo is impressive and production is unreliable
- You are locked into one provider and their pricing just changed
- Nobody can say what an AI request costs you per customer
- Latency is fine in testing and unacceptable on a real phone call
- Extraction accuracy is good enough to demo and not good enough to trust
Portability is designed in, not bolted on later
Model providers change pricing, deprecate endpoints and have outages. Teams that call a vendor SDK directly from product code discover the cost of that decision at the worst possible moment, usually during an incident or a renewal negotiation.
We put a provider boundary in from the first week. Product code speaks to our interface, adapters speak to vendors, and swapping one out is a contained change rather than a quarter of work. We have migrated a live voice product from one provider to another end to end — that is the level of portability we build toward by default.
- Provider-agnostic interface between product code and any model vendor
- Adapters per vendor so migration is contained rather than sprawling
- Fallback routing when a provider degrades or goes down mid-call
- Ability to run providers side by side to compare quality and cost on real traffic
Voice is a latency problem wearing an AI costume
Real-time voice is the most demanding AI surface to get right. A human on a phone call notices delay long before they notice a slightly weaker answer, so the whole pipeline has to be tuned for time-to-first-token rather than benchmark quality.
That means streaming everywhere, speculative work where it pays off, careful turn-taking and barge-in handling, and telephony integration that does not add its own delay. We have built this into live products — Twilio calling, real-time transcription, AI on the call, and messaging on the same customer record.
- Streaming pipelines tuned for time-to-first-token, not offline accuracy
- Turn-taking, interruption and silence handling that feels human
- Telephony integration with transcription and post-call structured output
- Graceful degradation and human handoff when the agent is out of its depth
Accuracy needs a review path, not just a better model
For document intake and extraction, the question is never whether the model is ever wrong. It is what happens when it is. If a wrong extraction silently writes to a record, you have built a system that quietly corrupts your customer's data.
We design confidence handling and human review into the pipeline from the start. High-confidence output flows through, uncertain output routes to a person, and every field keeps a link back to its source so a human can verify in seconds instead of re-reading a document.
- Confidence scoring with explicit thresholds per field, not one global setting
- Human-in-the-loop review queues for anything below the bar
- Source provenance retained so any value can be traced back and verified
- Correction feedback captured to improve the pipeline over time
Unit economics stay visible
AI features can quietly destroy margin. Token spend scales with usage, so the feature that looked cheap during a pilot becomes your largest infrastructure line once adoption arrives.
We instrument cost per request, per feature and per customer from the beginning, with caching and model tiering where cheaper models are genuinely good enough. You should be able to answer what an average customer costs you to serve — before finance asks.
How the work runs
Pipeline audit
We measure what exists: latency distribution, failure modes, cost per request and where accuracy actually breaks. Assumptions get replaced with numbers before anyone proposes a fix.
Boundary and instrumentation
The provider abstraction and cost or quality telemetry go in early, because every later decision depends on being able to measure and swap.
Harden the path
Failure handling, fallbacks, review queues and latency work on the highest-traffic surface first, validated against real traffic rather than test fixtures.
Extend and document
New AI surfaces built on the now-proven foundation, with runbooks so your team can operate and debug the pipeline independently.
What you get
- —Provider-portable AI layer with working adapters for your chosen vendors
- —Latency, cost and quality instrumentation per feature and per customer
- —Failure handling, fallback routing and human review paths
- —Voice, transcription and messaging integration where in scope
- —Runbooks covering debugging, tuning and provider migration
What we will not do
- —We do not train foundation models — we build the product engineering around them
- —We will not ship an AI feature we cannot instrument for cost and failure
- —We will say when a rules-based approach beats a model for your use case
Where we have done this
Common questions
Which providers do you work with?
Whichever fits the workload, and we build so the answer can change. We have shipped on multiple voice and model providers and migrated a production product between them without a rewrite.
Can you take over an AI prototype someone else built?
Frequently. Prototypes usually need the same three things: a provider boundary, real failure handling, and instrumentation. The model work is often the part that needs least change.
How do you keep AI costs predictable?
Per-request cost tracking, caching where responses repeat, and tiering so cheap models handle the easy majority. Predictability comes from measurement first, optimisation second.
Need this on your product?
Tell us where delivery is stuck. We will give you an honest read on scope and shape before anyone talks contracts.
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