Generative AI Applications
Generative AI Development - Beyond the Chat Box
Our generative AI development services build LLM-powered capability into products and processes: document drafting and summarization at scale, structured extraction from contracts, invoices and reports, brand- and compliance-controlled content generation, and multimodal workflows combining text, image, and data.
What We Build
Most organizations meet generative AI through a chat interface and conclude that's what it is. The larger value usually sits somewhere less visible: the extraction pipeline that reads ten thousand contracts, the summarization step that turns a two-hour call into a structured CRM record, the drafting engine that produces the first version of every proposal. Those systems don't demo as dramatically. They just quietly change what a team can produce in a week.
Document Intelligence
Reading, classifying, and extracting structured data from contracts, invoices, claims, reports, and forms - including the scanned and badly formatted ones that defeated your last OCR project. Output goes into your systems as clean structured data, with confidence scores and human review for low-confidence cases.
Summarization at Scale
Meetings, calls, tickets, research, and long documents condensed into consistent formats your teams can act on - with the source retained so anyone can verify a claim.
Content Generation with Controls
Drafting product descriptions, proposals, reports, and localized variants inside brand voice and compliance boundaries, with human approval where the stakes require it. Generation without review isn't efficiency; it's a liability distributed at speed.
Search and Knowledge Synthesis
Semantic search over your corpus that answers questions rather than returning ten links - with citations.
Multimodal Workflows
Combining text, images, and structured data: inspection photo analysis, visual quality checks, chart and diagram interpretation.
The Architectural Stance We Take
Model-agnostic by design. OpenAI, Anthropic, Google, and open-weight models each win in different scenarios, and the ranking changes every few months. We build behind an abstraction layer so you can switch models on quality, cost, latency, or data-residency grounds without rebuilding your application. Model lock-in is technical debt you choose on day one.
Evaluated, not assumed. Every generative feature ships with a test set and scored quality gates, because "it looked good in the demo" is not a quality standard. Our Labs team runs head-to-head model selection on your actual workload.
Cost-engineered. Prompt efficiency, caching, model tiering (small models for easy cases, large for hard), and batching. Naive implementations routinely cost several times what a well-engineered one does for the same output quality - and that gap compounds every month you run it.
Governed. Data-flow design, PII handling, logging, and human oversight aligned with our security and AI governance practice.
Document Intelligence in Practice
Extraction projects succeed or fail on the same details, and they're rarely the ones in the vendor demo.
Document variety is the real difficulty. A hundred invoices from one supplier is a simple problem. A hundred invoices from eighty suppliers, some scanned at an angle, some photographed on a phone, several in other languages, is the actual problem - and it's the one most organizations have.
Confidence thresholds decide the economics. Extraction with human review of low-confidence cases delivers value immediately. Extraction with no review is a liability. The threshold determines your review load, and tuning it is a business decision about error tolerance, not a technical one.
Validation beats accuracy. Cross-checking extracted values against business rules - does the line-item total match the invoice total, does the supplier exist, is the date plausible - catches errors that no model-level accuracy figure predicts.
Exceptions need a home. The five to fifteen percent of documents that don't process cleanly still need handling. A pipeline without a well-designed exception queue simply moves the bottleneck.
We measure accuracy on your documents during a pilot rather than quoting a headline number, because published figures come from clean benchmark sets that resemble nobody's post room.
Have a Document Pile or a Content Bottleneck?
Bring one real process. We'll tell you what's automatable today, what isn't, and what it would cost to run.
Controlling Quality at Volume
Generation is cheap, which is precisely the risk. Volume without control produces reputational damage at speed. Our controls, applied proportionally to the stakes:
Grounding - generate from approved source material rather than model memory, so claims trace to something real
Brand and style encoding - voice, terminology, and prohibited claims defined in the system rather than hoped for
Automated checks - factual consistency against sources, prohibited-claim detection, reading level, format compliance
Human approval gates - mandatory for anything customer-facing, regulated, or contractual
Sampling and audit - ongoing review of a percentage of output even where full review isn't feasible
Provenance logging - what was generated, from what sources, by which model version, reviewed by whom
That last item is increasingly a compliance requirement, not just good practice, and retrofitting it is painful. Build it in from the start.
Where Generative AI Disappoints
Being direct about this saves everyone money:
Tasks needing guaranteed accuracy without review. If no error rate is acceptable and review is infeasible, generative AI is the wrong tool.
Domains with sparse public knowledge and no internal documentation. Retrieval needs something to retrieve. If the expertise lives only in people's heads, capture it first.
Very low-volume tasks. Engineering, evaluation, and maintenance cost more than the time saved. This is genuinely common and rarely admitted by anyone selling a build.
Work whose value is the human doing it. Some outputs are valued precisely because a person produced them. Automating those saves time and destroys the thing being paid for.
Frequently Asked Questions
It depends heavily on document variety, scan quality, and field complexity - which is why we measure on your documents in a pilot rather than quoting a benchmark figure. Production systems pair measured accuracy with confidence thresholds that route uncertain cases to human review, plus business-rule validation.
When guaranteed accuracy is required and review isn't feasible; when the necessary expertise isn't documented anywhere; when volume is too low to repay engineering and maintenance costs; and when the value of the output comes specifically from a human having produced it.
Building applications powered by generative models - usually large language models - that produce text, structured data, images, or code as part of a business process. It spans prompt and retrieval engineering, integration, evaluation, and cost and safety governance.
Whichever performs best on your specific task at acceptable cost and latency, tested rather than assumed. Because model leadership shifts frequently, the more important decision is architecting so you can change providers without rewriting your application.
Accuracy varies by document type, quality, and field complexity - which is why we measure it on your documents during a pilot rather than quoting a headline number. Production systems pair measured accuracy with confidence thresholds that route uncertain cases to human review.
Not if the deployment is architected for it: enterprise API terms that exclude training on your data, private cloud deployment, or self-hosted open-weight models, with PII handling and logging designed in. Data-flow design is part of every engagement.
Through brand and style guidance encoded in the system, retrieval from approved source material, automated quality checks, and human approval gates for externally published content. Volume without review is where reputational damage happens.
Ongoing cost depends on volume, model choice, and prompt efficiency. We model expected run cost during design - and engineer against it - because a feature that's cheap to build and expensive to run is a problem you discover in month three.