The prototype impressed. Nobody trusts it in production.
A demo built on a few hand-picked questions rarely survives contact with real users. Without an evaluation set, every prompt change is a guess, and regressions surface as customer complaints.
AI DEVELOPMENT
AI systems engineered for the real world.
NeuralSharks is an AI engineering company, not a prompt-and-API-key agency. We build the retrieval, orchestration, evaluation, and operations that turn a model into a system you can depend on.
Calling an LLM API takes an afternoon. Knowing whether the answer is right, whether the user was allowed to see its source, what it cost, and what happens when the provider is down takes engineering. That is the work we do.
01 / BEYOND THE API CALL
Calling a model is the easy part.
An AI wrapper sends a prompt to a model and shows whatever comes back. An engineered AI system knows where its answer came from, what it cost, and what to do when it’s wrong.
THE DEMO · PSEUDOCODE
const answer = await llm.generate(prompt + question);
return answer;Two lines. It works in a demo, and it leaves every production question unanswered. Each question on the right is a piece of engineering.
- 01
Which documents is this user allowed to see?
Permission-aware retrieval
- 02
Is the answer actually supported by a source?
Grounding, citations, and faithfulness checks
- 03
What if a retrieved document contains instructions?
Prompt-injection guardrails
- 04
Will the output parse in the system that consumes it?
Structured output and schema validation
- 05
Did the last prompt change make anything worse?
Evaluation datasets and regression gates
- 06
What does one request cost at 100× the volume?
Token budgets, caching, and model routing
- 07
What happens when the provider times out?
Fallback models and graceful degradation
- 08
Who checks it before it reaches a customer?
Human-in-the-loop workflows
02 / WHY TEAMS CALL US
The problems we’re usually hired to solve.
Most organizations don’t need convincing that AI could help. They need it to work reliably, safely, and inside their existing systems.
Answers sound confident and cite nothing.
Teams need responses grounded in their own documents, with references a reviewer can check, and an honest “I don’t know” when the source material doesn’t cover the question.
Cost and latency are unknown until the invoice arrives.
Model choice, context size, caching, and retrieval depth all drive cost per request. Without measurement, growing usage is a financial risk rather than a plan.
Security and legal haven’t signed off.
Access control, data residency, prompt injection, and audit trails need clear answers before sensitive data goes anywhere near a model, not after a pilot is already live.
The AI lives outside the tools people actually use.
Value comes from integration with the CRM, ticketing system, document store, or internal platform where work already happens. A separate chat window seldom changes how a team operates.
Nobody owns it after launch.
Models are updated, data changes, and behavior drifts. Someone needs the tracing, evaluation suite, and runbooks to keep the system working and to improve it safely.
03 / WHAT WE BUILD
Systems with a job to do.
Typical systems we design and build. Each one is shaped around your data, users, and constraints rather than a template.
Generative AI applications
Drafting, summarization, rewriting, and analysis tools built around a defined task, with review steps and output checked against your own standards.
LLM-powered products
AI features inside your SaaS product, such as natural-language querying, assistants, and smart defaults, with per-tenant isolation, usage metering, and product-grade UX.
RAG systems
Assistants that answer from your documents, tickets, and databases, cite the passages they used, respect permissions, and decline when the sources don’t cover the question.
AI agents
Bounded agents that plan and call tools to complete multi-step tasks, with least-privilege access, approval gates, and a trace of every action taken.
AI search
Search that understands intent: hybrid keyword and semantic retrieval, query rewriting, re-ranking, and answer summaries, tuned against real queries.
Document intelligence
Classification and field extraction from contracts, invoices, claims, and forms into validated, typed records, with low-confidence items routed for review.
Recommendation systems
Recommendations that combine behavioral signals with embeddings and model-based re-ranking, with explanations users understand and offline metrics before launch.
Workflow intelligence
Classification, triage, routing, and summarization inside operational processes, with deterministic rules where exactness matters and an exception path back to a person.
AI integrations
Model capability connected to your CRM, ERP, ticketing, and data platforms through typed APIs, event streams, and idempotent writes.
04 / ARCHITECTURE
Seven layers, and the model is only one.
A reference architecture for production AI. Each layer has its own failure modes, and each one emits signals that evaluation and monitoring use to keep the whole system honest.
- L1
Application
Where people and systems meet the AI: interfaces, product APIs, streaming, and feedback capture.
- Web & mobile UI
- Product APIs
- Streaming
- Feedback
TRACEDuser, tenant, session, feedback
request + identity + context
- L2
AI orchestration
Decides what happens on each request: routing, prompt assembly, tool loops, guardrails, retries, and output validation.
- Versioned prompts
- Router
- Tool loop
- Guardrails
- Schema validation
TRACEDroute, prompt version, guardrail verdicts
assembled prompt + tool schemas
- L3
LLMs / models
Generation, embedding, and re-ranking models, chosen per task and wrapped in a fallback chain.
- Hosted models
- Open-weight models
- Embeddings
- Re-rankers
- Fallback chain
TRACEDmodel, tokens in/out, latency, cost
retrieval query
- L4
RAG / vector search
Finds the right context: hybrid search, metadata and permission filters, and re-ranking over indexed content.
- Hybrid search
- Vector index
- Permission filters
- Re-ranking
TRACEDchunk IDs, scores, filters applied
authorized, current records
- L5
Business data
The source of truth: documents, databases, and systems of record, kept in sync by ingestion pipelines.
- Documents
- Databases
- CRM / ERP
- Ingestion & sync
TRACEDsource, version, freshness
typed function calls
- L6
External tools / APIs
Where the system acts: typed functions with least-privilege credentials, idempotent writes, and approval gates.
- Typed functions
- Scoped credentials
- Idempotent writes
- Approval gates
TRACEDtool, arguments, result, approver
traces + scores from every layer
- L7
Evaluation / monitoring
Watches every layer above: offline evaluation in CI, online quality sampling, tracing, drift detection, and cost and latency alerts.
- Eval suites in CI
- Online sampling
- Tracing
- Drift detection
- Alerts
PRODUCESrelease gates, alerts, dashboards
05 / CAPABILITIES
The engineering inside every system.
Each capability can stand alone, but production systems usually need most of them working together.
Structured output
Model responses constrained to JSON schemas, validated by typed parsers, and repaired or retried on failure, so downstream code receives data rather than prose.
Function and tool calling
Typed tool definitions, argument validation, timeouts, and idempotent execution, so a model can query systems and take actions inside defined permissions.
Vector search
Chunking strategy, embedding model choice, hybrid keyword and vector retrieval, metadata filters, and re-ranking, tuned for recall and precision on your own queries.
Evaluation
Golden datasets, automated scorers, model-graded checks calibrated against human review, and regression gates in CI for every prompt, model, or retriever change.
Guardrails
Input and output validation, prompt-injection defenses, PII detection and redaction, policy boundaries, and defined behavior when confidence is low.
Observability
Traces across retrieval, model calls, and tools, with prompt versions, latency percentiles, token usage, and user feedback linked to the exact request.
Cost optimization
Token budgets, prompt and semantic caching, context trimming, batching, and routing simple requests to smaller models, measured as cost per successful task.
Model selection
Hosted and open-weight models compared on your evaluation set for quality, latency, cost, and data handling, behind an abstraction that keeps switching cheap.
Need a system that takes actions, not just answers? Or AI inside an operational process? AI agents · AI automation
06 / PRODUCTION CONCERNS
The hard parts, solved before launch.
Where AI products usually fail in production, what we put in place for each, and the number that tells us it’s working.
Data privacy
FAILURE MODESensitive data reaches a provider, a log, or a user who shouldn’t see it.
- Data scope and classification agreed before ingestion
- Permission filters applied inside the retrieval query
- PII redacted from prompts, traces, and logs
- Provider, region, and retention matched to your obligations, with self-hosted models where required
WE MEASUREAccess-control test pass rate; no unredacted PII in traces
Hallucination mitigation
FAILURE MODEFluent, confident answers with no basis in your sources.
- Answers grounded in retrieved passages, with citations
- Explicit permission to say “I don’t know”
- Faithfulness checks that compare claims to sources
- Consequential outputs routed to a person
WE MEASUREFaithfulness and citation accuracy on the evaluation set
Retrieval quality
FAILURE MODEThe right answer exists in your data, but the system never finds it.
- Chunking that follows document structure
- Hybrid keyword and vector search with re-ranking
- Query rewriting and metadata filters
- Retrieval evaluated separately from generation
WE MEASURERecall@k and MRR on labelled queries
Prompt architecture
FAILURE MODEOne sprawling prompt that nobody dares to change.
- System, task, context, and output instructions kept as separate, versioned components
- Retrieved content delimited as untrusted data
- Examples drawn from the evaluation set
- Prompts released and rolled back like code
WE MEASUREEvaluation score change per prompt version, before release
Evaluation datasets
FAILURE MODEQuality judged by whoever tried it last.
- Representative, edge-case, and adversarial examples from real usage
- Reference answers and rubrics agreed with domain experts
- Model-graded scoring calibrated against human labels
- Production failures added to the set every release
WE MEASUREPass rate by category, tracked release over release
Latency
FAILURE MODEThe answer arrives after the user has given up.
- Streaming responses and early partial output
- Retrieval and tool calls run in parallel
- Smaller, faster models for routing and classification steps
- Caching for embeddings and repeated requests
WE MEASUREp50 / p95 time to first token and to complete answer
Token cost
FAILURE MODEUsage grows faster than the budget.
- Cost model built during architecture, not after launch
- Context trimmed to what the task needs
- Prompt caching and semantic caching
- Per-feature and per-tenant budgets with alerts
WE MEASURECost per successful task, by feature and tenant
Fallback models
FAILURE MODEA provider outage or rate limit becomes your outage.
- Fallback chain across providers or model sizes, evaluated in advance
- Timeouts, retries with backoff, and circuit breakers
- Graceful degradation to search results or a human queue
- Fallback paths exercised in tests, not just configured
WE MEASUREAvailability, fallback rate, and quality on the fallback path
Human-in-the-loop workflows
FAILURE MODEAutomation makes a consequential decision nobody reviewed.
- Confidence thresholds route uncertain cases to review
- Approval gates before writes, sends, or payments
- Review screens that show sources and proposed actions
- Reviewer corrections fed back into evaluation
WE MEASUREReview rate, override rate, and time to decision
07 / ENGINEERING APPROACH
Engineered like the rest of your critical software.
AI systems fail in new ways, but they still need the disciplines that keep any production system dependable.
01
Architecture
The model is a component, not the system.
- Retrieval, orchestration, model access, and business logic separated behind clear interfaces
- Models and vector stores replaceable without a rewrite
- Deterministic code for anything that must be exact
02
Scalability
Designed for growth in users, data, and cost.
- Queues and asynchronous processing for long-running work
- Caching for embeddings and repeated requests
- Rate-limit handling, provider fallbacks, and a cost model before usage grows
03
Testing
Two kinds of tests, both in CI.
- Unit and integration tests for the software around the model
- Evaluation suites built from representative and adversarial examples
- Regressions block a release instead of reaching users
04
Observability
Every answer can be explained after the fact.
- End-to-end traces: retrieved context, prompts, responses, and tool calls
- Latency, token usage, and cost per request
- User feedback linked to traces, with sensitive data redacted
05
Security
Retrieved content and user input are untrusted.
- Least-privilege access to data and tools
- Permission checks at retrieval time, not just at login
- Secrets management, audit logs, and data handling matched to your obligations
06
Deployment
Releases are routine, reversible, and observed.
- Infrastructure as code and versioned prompts and configuration
- Staged rollouts behind feature flags
- Fallback behavior and runbooks in place before launch
Infrastructure, pipelines, and monitoring are delivered with our cloud and DevOps practice; connections to your systems with data and integrations.
08 / TECHNOLOGIES
Tools chosen for the problem.
We recommend a stack after discovery, based on your requirements, existing platforms, and team.
Models
Selected per task on your evaluation set, not by brand.
Retrieval & data
Search quality is usually the biggest lever on answer quality.
Orchestration & quality
Thin, testable layers between your product and the model.
Application & infrastructure
Deployed in the environment your compliance requires.
Representative technologies; no official partnerships are implied. Explore our technology capabilities.
09 / PROCESS
Seven stages, each with an output.
Every stage ends with something you can review, so decisions are made on evidence rather than optimism.
- 01
Discovery
Clarify the task, users, data sources, and constraints. Collect real examples and agree on what a good, and an unacceptable, answer looks like.
OUTPUTUse-case brief, evaluation criteria, feasibility view
- 02
Architecture
Choose the retrieval approach, candidate models, integration points, and data boundaries. Estimate latency and running cost.
OUTPUTArchitecture decisions and cost model
- 03
Design
Design the experience around trust: how sources are shown, how uncertainty is communicated, and where people review or override.
OUTPUTInteraction design and review workflow
- 04
Implementation
Build in short iterations against the evaluation set, integrating with your systems early so problems surface while they are cheap.
OUTPUTWorking increments on your data
- 05
Testing
Run evaluation suites, adversarial cases, load tests, and security review alongside conventional software tests.
OUTPUTEvaluation report and release recommendation
- 06
Deployment
Release in stages with monitoring, alerts, fallbacks, and runbooks in place. Hand over to your team where that is the plan.
OUTPUTProduction release and operating documentation
- 07
Optimization
Use traces and feedback to improve retrieval, prompts, and model choice, reduce cost and latency, and add real failures to the evaluation set.
OUTPUTMeasured improvement, release over release
See how this fits into our wider delivery process.
10 / RELEVANT WORK
How we think it through.
Client case studies are published once they are approved. The work below consists of illustrative concepts, not delivered engagements or claimed results.
Illustrative concept
Knowledge, connected.
A design study for a private, permission-aware answer layer over fragmented organizational knowledge.
- PROBLEM
- Answers exist across wikis, drives, and ticket history, but people cannot find them quickly, and search ignores who is allowed to see what.
- SOLUTION
- Retrieval-augmented knowledge assistant
- INTENDED OUTCOME
- Grounded answers with visible sources, scoped to each person’s existing permissions, and a clear route to a human expert when the system cannot answer.
Illustrative concept
Documents, triaged.
A design study for AI-assisted document intake, with extraction people can verify and review where confidence is low.
- PROBLEM
- Incoming documents are read, classified, and keyed in by hand; volumes spike unpredictably and errors are caught late.
- SOLUTION
- AI-assisted document intake and extraction
- INTENDED OUTCOME
- Routine documents processed with human review where confidence is low, and every extracted field traceable to the text it came from.
- InsightThe distance between an AI demo and production.Why evaluation sets, untrusted-input boundaries, request tracing, and fallbacks belong in the first architecture discussion for an AI product, not the post-launch backlog.
- IndustryProfessional servicesKnowledge discovery, document workflows, and connected client operations.
- LabsAI document intelligenceExtracting structured, verifiable fields from messy business documents.
11 / WHY NEURALSHARKS
What working with us looks like.
Commitments you can hold us to during an engagement.
Engineering leadership
NeuralSharks was founded by six software engineering partners. Senior engineers make the architectural decisions, stay involved in delivery, and record each decision with the trade-offs behind it.
About NeuralSharksQuality as scope, not a phase
Evaluation sets, automated tests, and release criteria are planned from the first iteration. Quality engineering is an in-house discipline, not something bolted on before launch.
Software testingCommunication you can act on
You see working software on your own data at a regular cadence, hear about risks when we find them, and get trade-offs explained in plain language.
Product thinking
We start from the task a user needs to complete. Sometimes the right answer is a smaller model, a better search index, or no AI at all, and we would rather tell you early.
End-to-end capability
AI, software engineering, data integration, quality, and cloud operations sit in one team, so the model, the product around it, and the infrastructure beneath it are designed together.
All services12 / FAQ
Questions to ask before you hire us.
If yours isn’t here, ask us directly. A straight answer is part of the service.
What makes you an AI engineering company rather than an AI agency?
Most of a production AI system is not the model call. It is retrieval, permissions, orchestration, evaluation, guardrails, integration, observability, and cost control. Our team are software engineers first: we design those parts with the same discipline as any other critical system, and we judge quality by measurements on your data, not by a demo.
How do we know whether our use case is a good fit for AI?
Strong candidates involve language or unstructured information, can tolerate some variability with a review step, and come with real examples we can evaluate against. If a task needs an exact, deterministic answer every time, rules or conventional software are often better. Discovery tests this before significant investment.
Do you train custom models?
Usually not as a first step. Most business problems are solved by existing models combined with good retrieval, clear instructions, and structured outputs. Fine-tuning can make sense for consistent formatting, specialized classification, or cost and latency at high volume, and we recommend it when evaluation shows it beats the simpler approach.
Which model or provider will you use?
We compare candidate models on your evaluation set for quality, latency, cost, and data-handling requirements, then choose per task. A thin provider abstraction means switching later is an evaluation exercise rather than a rewrite.
What happens to our data?
Data handling is agreed up front: which sources are in scope, where data is processed and stored, which providers and regions are acceptable, retention periods, and how access permissions are enforced. Where requirements rule out third-party APIs, self-hosted open-weight models are an option.
How do you deal with hallucinations?
They can be reduced and contained, not eliminated. We ground answers in retrieved sources with citations, allow the system to decline when sources are insufficient, validate structured outputs, measure faithfulness in evaluation, and route consequential decisions to a person.
How long does a project take?
It depends mostly on data readiness and integration complexity, so we estimate after discovery rather than before. The first milestone is always a working system evaluated on your data, which lets you decide on evidence whether to continue to production.
Can you work alongside our engineering team?
Yes. We can work in your repositories, CI pipelines, and cloud accounts, pair with your engineers, and document the system so your team can own it. Handover is planned from the start rather than left to the final week.
What will it cost to run once it’s live?
Running cost depends on volume, model choice, context size, and caching. We build a cost model during architecture and instrument cost per request in production, so spend is visible and controllable rather than a surprise.
START A PROJECT
Build an AI product that survives production.
Tell us what you’re building, what data it depends on, and where the risk is. We’ll come back with questions, an honest view on feasibility, and a first milestone evaluated on your own data.