An AI development company that builds for production, not the demo
LLM applications, generative AI features and document agents that run inside real operations, with the evaluation sets, audit trails and human review that let them stay there.
- Python
- Node.js
- React
- PostgreSQL with pgvector
- AWS
- Commercial and open-weight LLMs
- OCR and document AI models
- Real business data
- Source documents
- Policies and rules
- 01 Retrieve contextby version and effective date
- 02 Model proposesstructured fields with cited sources
- 03 Rules decideversioned, repeatable outcome
- 04 Human reviewlow-confidence results to a reviewer
- Production AI feature
- Per-decision audit record
- Workflow dashboards
What you get
7 deliverables, all yours to keep
- Production LLM application on your real data
- Evaluation and regression set from real cases
- Structured output schemas and versioned prompts
- Per-decision audit records
- Reviewer screen and exception queue
- Cost, latency and accuracy dashboards
- Written permitted-output policy
Sound familiar?
Who hires an AI development company, and what they bring to us
Most teams that call an AI development company have already built something that worked in a notebook or a demo and never reached the process it was meant for. We build the system around the model: evaluation sets, structured outputs, review queues, audit records and integrations, so an LLM application can run inside a real business process.
For a large-scale energy enterprise, that meant a partner onboarding platform where automated KYC checks handled high-volume verification and people handled the exceptions. Platforms of this type reduce onboarding time by up to 45%. We have written up how the document agent inside it is built. If you need a chatbot on a marketing site, a packaged tool will likely serve you better than custom AI development services.
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A generative AI pilot stalled at the compliance review
Compliance asked how a wrong answer would be caught and who would be accountable for it, and the pilot had no answer.
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Teams read, check and re-key documents all day
A proof of concept shows a model can do most of it, but nobody trusts "most".
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An internal assistant answers from the wrong policy version
It answers questions from policy documents, and sometimes from a version of the policy that no longer applies.
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A paid AI feature has to behave the same next quarter
Customers expect stable behavior after the model provider ships an update.
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A strong team that has not run an LLM app in production
They do not want to learn evaluation, drift and prompt injection on a live customer workflow.
The work
Inside a AI Development engagement.
LLM application development and generative AI features we build
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Document agents
Classify, extract and cross-check fields from licenses, invoices, registrations and bank letters, with confidence scored per field.
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Policy and approval assistants
Read a request, cite the clause it may breach, and hand a structured finding to a rules engine.
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Retrieval applications
Answer from your own documents, retrieving by version and effective date rather than by similarity alone.
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Generative AI inside products
Drafting, summarization, classification and search features behind an interface your users already know.
How we approach AI development services
- 01
The evaluation set comes first
A few hundred real, anonymized cases with agreed correct outputs. Every prompt edit, retrieval change or model upgrade runs against it.
- 02
The model does not make the decision
It produces structured output: extracted facts and flags with cited sources. A versioned rules engine or a named person decides, so the same facts give the same outcome.
- 03
Confidence is earned per field, not declared per document
Fields are cross-checked against the submitted form or a reference source. Thresholds start conservative and come down only as correction data justifies it.
- 04
We record what the model saw
Each decision stores the rendered prompt, retrieved context, model and prompt versions, raw output and the rules version applied.
- 05
Model choice stays reversible
The model sits behind an interface, versions are pinned per workflow, and an upgrade is a release with a regression run and a sign-off.
Where AI projects go wrong, and how we avoid it
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A confidently wrong answer: plausible, well formatted and raising no error
Cross-checks against structured sources are designed in from the start, rather than hoping reviewers notice.
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Prompt injection arriving through the data, such as an invoice comment field
A fixed output schema, a rules engine that ignores anything outside it, and logging of any stray text.
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Scope creep: a model that summarizes gets asked to recommend, then to decide
We write down per workflow exactly what the model may produce. Changing that list goes through your risk owner.
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Building the wrong thing well when the delay sits in queueing, not checking
We time the current cycle before committing. Sometimes the honest recommendation is a workflow fix with no AI in it.
How it runs
From first call to production.
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Pick the workflow
One use case with a cycle time or cost you can put a number on. We time the current process before we design anything.
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Build the evaluation set
A few hundred real, anonymized cases with agreed correct outputs. It becomes the test every later change has to pass.
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Prototype against real data
Two to three model and retrieval approaches compared on the evaluation set, with cost per call and latency recorded alongside accuracy.
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Engineer the system around the model
Structured outputs, rules, review queue, audit records, access control and integration with the systems the result has to reach.
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Release with humans in the loop
Conservative thresholds at launch, lowered field by field as reviewer corrections show where the model can be trusted.
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Operate and extend
Model upgrades handled as releases, drift watched per workflow, and the second use case built on the governance layer the first one created.
Team and timeline
4–6 weeks
to a production release
Indicative. Actual duration depends on requirements and complexity, and can change.
Who works on it
- Solution architect
- Backend, AI and integration engineers
- UI designer
- QA engineer
- Delivery lead
Engagement model
Time and Materials for the first use case, where the evaluation results decide the design. Fixed Cost once the pattern is proven and the next workflow is well understood.
Good to know
Your side supplies a process owner, access to sample data, and a risk or compliance contact who joins threshold and permitted-output decisions early. That last person saves more time than anyone else.
Proof
Where we have built this.
How a leading energy enterprise reduced partner onboarding time by 45%
faster partner onboarding
How a leading construction enterprise improved vendor processing by 42%
faster vendor processing
How a large global financial services company reduced travel processing time by 38%
faster travel processing
FAQ
AI Development: common questions.
What does an AI development company actually build?
Software with a model inside it. The model is usually the smallest part. Most of the effort goes into retrieval, structured outputs, the rules that act on those outputs, the review screen for uncertain cases, audit records, integrations and monitoring. A good AI development company spends more time on that system than on prompts, because the system is what decides whether the feature can run near a real business process.
How long does it take to put an LLM application into production?
For one well-scoped use case, typically 4–6 weeks to a production release with humans reviewing low-confidence results. The dates that matter most sit early: access to sample data for the evaluation set, and a compliance contact who agrees permitted outputs before the design depends on them. We book both in the first week.
Should we use a commercial model API or an open-weight model?
Start with whichever passes your evaluation set at an acceptable cost, and design so you can switch. Commercial APIs are faster to start with. Open-weight models make sense when data cannot leave your environment, when volume makes per-call pricing expensive, or when you need to pin behavior for years. We keep the model behind an interface so the choice is reversible.
How do you stop an AI feature from giving wrong answers?
You cannot stop it entirely, so you design for it. The model returns structured fields rather than free text, those fields are cross-checked against source data where possible, and disagreement or low confidence routes the case to a person. Consequential decisions are made by deterministic rules or a named reviewer, never by the model alone. Every output is logged so errors can be traced.
Can you work with our in-house data science or engineering team?
Yes, and it is often the best setup. Your team knows the data and the business rules. We bring the production engineering: evaluation harnesses, review workflows, audit records and deployment. A common arrangement is our engineers pairing with yours through the first use case so your team owns and extends the system afterward.
Related services
- AI Workflow AutomationApprovals, onboarding and verification rebuilt as governed workflows. AI handles volume, rules keep decisions repeatable, and people own the exceptions.
- Intelligent Document ProcessingDocument AI for invoices, KYC files and registrations. Per-field confidence, cross-checks against source data, and a reviewer queue for anything the model is unsure of.
- AI ConsultingAI strategy, readiness assessment and governance design from engineers who ship production AI, including EU AI Act readiness for systems that reach European users.
