Pilots without context fail under real data
A prompt-and-pray demo collapses the moment messy production data shows up.
AI Solutions
AI Solutions & LLM Integration
Production AI systems — not demos that never reach operations.
We build AI features that get deployed and used: document automation, retrieval-augmented assistants, agent workflows, and AI that lives inside the products your team already runs.
Outcomes
AI that ships to productionWorkflow automationMeasurable ROIIndustries
FintechLegalOperationsB2B SaaSThe Problem
Most AI projects look great in demos and never reach production.
No control layer around the model
Without validation, retrieval, and review, AI outputs can't be trusted by the business.
Integrations stop at the API call
Real value comes from wiring AI into existing workflows — CRMs, ticketing, dashboards, ops tools.
No path from prototype to production
POCs without monitoring, eval sets, and rollback plans never make it past the experiment phase.
Our Approach
Build the system around the model, not just the model.
Step 1
Workflow audit
We map the business process, identify the highest-ROI automation point, and validate that AI is actually the right answer.
Step 2
Data + retrieval
We structure the source data, build the retrieval layer, and validate context quality before tuning the model.
Step 3
Build with guardrails
Validation, schema enforcement, eval sets, and human review — every AI output earns its way through controls.
Step 4
Deploy + measure
Production rollout with logging, monitoring, eval-set regression checks, and a feedback loop into improvements.
Deliverables
What ships at the end of the engagement.
Every engagement closes with a working production system, documentation, and a handover so your team owns it after we step out.
- AI workflow architecture and orchestration layer
- Retrieval-augmented generation (RAG) pipeline
- Custom AI agents wired into your existing tools
- Eval framework + monitoring dashboards
- Human-in-the-loop review interfaces
- Documentation and team handover
Use Cases
Where this service creates real leverage.
Document → structured data
Turn PDFs, scans, and forms into validated outputs that feed downstream systems.
Less manual entry, faster turnaround, cleaner operations.
Internal AI assistant
Convert SOPs, runbooks, and Slack history into an answer engine your team can trust.
Faster answers, better execution, less internal friction.
Agent for repeatable ops
Automate intake, triage, validation, escalation, and routing across customer-facing workflows.
More throughput without adding headcount.
AI features inside your product
Embed AI features customers will actually pay for — drafting, summarization, semantic search.
Higher product value and a defensible moat.
Tech Stack
- OpenAI
- Anthropic
- LangChain
- pgvector
- Python
- Node.js
- Postgres
- Redis
Why SofGent
Built for teams that need real systems, not demos.
We build production AI, not experiments
Reliability, monitoring, and controls are part of the build from day one.
Engineering + AI under one team
We own the data, model, interface, and operations — no fractured handoffs.
ROI-driven scope
We pick the workflow with the clearest financial impact, not the fanciest demo.
Vendor-neutral
OpenAI, Anthropic, open-source — we recommend what fits the use case, not what we resell.
Pricing
From $25,000
Architecture sprint + build
Most engagements ship the first production workflow in 4 weeks.
FAQ
Answers to the questions clients ask before they book.
Don't see your question? Mention it on the strategy call — we'll cover the specifics for your stack and stage.
We're vendor-neutral. OpenAI, Anthropic, Google, open-source models (Llama, Mistral) — we pick what fits the task. Most production builds use OpenAI or Anthropic with a retrieval layer over your own data.
Retrieval-augmented generation forces the model to cite sources. Schema validation rejects malformed outputs. Eval sets catch regressions before deploy. And human-in-the-loop review handles the edge cases that aren't safe to automate.
Sometimes. We usually start with retrieval and prompting because they ship faster and are cheaper to maintain. We recommend fine-tuning only when retrieval has hit a measured ceiling.
We use vendor APIs with zero-data-retention enabled, run sensitive workloads in your own cloud account, and design retrieval pipelines so confidential data never leaves your perimeter.
AI projects start at $25,000 for the first production workflow. Cost scales with the number of integrations, the volume of data, and whether evals + monitoring are part of scope (we recommend they always are).
Ready to start
Let's scope your ai solutions & llm integration engagement.
Book a free 20-minute strategy call. We'll review your stack, surface the highest-ROI workflow, and outline a production path.