AI service · Generative AI
Generative AI & LLM Application Development
We build generative AI into products: copilots that draft inside your app, engines that turn raw data into readable reports, extraction pipelines that convert documents into clean structured records. Everything ships with evaluation baked in, because a feature users can't trust is a feature they stop using.
Free scoping call · Clear ROI plan before any commitment
The challenge
Shipping an LLM feature is easy; shipping one users trust is not. Early generative features impress in the demo, then wobble in production — inconsistent tone, hallucinated details, broken JSON, latency spikes, and API bills that scale faster than revenue. Product teams end up frozen between shipping something flaky and shipping nothing.
How we solve it
We treat prompts, model choices, and output contracts as engineered artifacts: versioned, tested against golden datasets, and monitored in production. Outputs are schema-validated, grounded in your data where facts matter, and routed across models to balance quality, latency, and cost. Your team gets a feature that behaves the same on Monday as it did in the sales demo — and the eval infrastructure to keep it that way as models evolve.
Capabilities
What we deliver
The building blocks of a production-grade generative ai engagement.
In-product copilots & assistants
AI that drafts, edits, and suggests inside your product's own context — aware of the user's data, tone, and task, not a generic chat bolted to the sidebar.
Content & report generation
Pipelines that turn structured data into polished output — product descriptions, personalized outreach, executive summaries, compliance narratives — at scale and on brand.
Structured extraction & classification
LLM pipelines that convert contracts, emails, and free text into validated, schema-conformant records your systems can act on.
Summarization & intelligence layers
Meeting, thread, and document summarization tuned to what your users actually need to know, with traceability back to the source.
Prompt engineering & LLM evals
Versioned prompt systems, golden-set regression testing, A/B evaluation, and quality dashboards — the infrastructure that keeps generative features reliable across model updates.
Model routing & cost optimization
Multi-model architectures that send each request to the cheapest model that meets the quality bar, with caching and batching to keep unit costs predictable.
How we work
A clear path to production
Five stages, each with visible output — you're never waiting on a black box.
Discovery & scoping
We map the problem, success metrics, constraints, and existing systems before writing code. You leave with a clear scope, timeline, and a fixed view of what 'done' means.
Architecture & design
We design the system end to end — data model, integrations, security, and a path to scale — and validate it against your real workloads, not a demo.
Iterative delivery
We ship in short, reviewable increments. You see working software every sprint, give feedback early, and never wait months to find out it missed the mark.
Hardening & launch
Testing, observability, performance, and security are built in, not bolted on. We launch with monitoring in place and a rollback plan ready.
Support & iteration
After launch we stay on — measuring outcomes, fixing fast, and iterating on what the data tells us actually moves the metric.
Representative stack
- Claude (Anthropic)
- OpenAI
- Vercel AI SDK
- LangSmith / Braintrust (evals)
- Structured outputs / JSON Schema
- TypeScript / Python
Where it applies
Generative AI in the industries we serve
Vertical context changes what good looks like — see how this capability lands in your space.
E-commerce
High-performance commerce, headless storefronts, and AI personalization that lift conversion.
Explore E-commerceEducation
LMS, course platforms, and AI tutoring built for engagement, accessibility, and scale.
Explore EducationFintech
Payments, lending, and financial platforms built for security, reliability, and compliance.
Explore FintechAnswers
Frequently asked questions
How do you prevent hallucinations in LLM features?
By grounding factual claims in retrieved source data, constraining outputs to validated schemas, keeping the model's job narrow, and measuring faithfulness against test sets on every change. For features where a wrong fact is costly, we add citation requirements and human review paths.
Which LLM is best for our product?
It depends on the task, and it changes — which is why we build model-agnostic and benchmark on your data. Typically frontier Claude or GPT models handle complex reasoning while smaller models serve high-volume simple tasks. Routing between them is often the difference between a viable and an unviable cost structure.
Can generative features run on our own infrastructure?
Yes. Open-weight models served via vLLM in your cloud can power many generative workloads where privacy or cost demands it. We'll benchmark whether an open model meets your quality bar before recommending that path.
How do you handle LLM API costs at scale?
We design for unit economics from the start: measure cost per feature-use, cache repeated context, batch where latency allows, and route to cheaper models for simpler requests. You get a cost dashboard alongside the quality dashboard, so trade-offs are explicit.
Ready to build with Generative AI?
Book a free consultation and we'll map the fastest path to a working system — with the metric that proves it.