Software Development and AI
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Custom software development and marketing technology for enterprise
Oligamy Marketing is the marketing branch of Oligamy Software , a software house with deep roots in advanced application development, custom infrastructure, and enterprise grade AI. We did not learn to talk about AI. We built it.
The Difference: Our software division created a fully custom LLM for a corporate client that autonomously merged and classified tens of thousands of contracts and invoices. After the first production training instance the model reached 96% accuracy. After the second fine tuning round it reached 99%. What used to require a team of six specialist reviewers now runs unattended, freeing over 340 employee hours per month and eliminating the error rate from ~15% to under 1%.
Our LLM Development Process
Discovery and Architecture
Map document types, data flows, security boundaries, and integration points across client infrastructure.
Data Preparation and Training
Curate and structure the training corpus from client documents, configure fine tuning pipeline and accuracy baselines.
Validation and Fine Tuning
Benchmark accuracy per document category, iterate training rounds until target precision is reached and verified.
Production and Security Hardening
On premise deployment with role based access, full audit logging, and ongoing model monitoring.
99% accuracy. From scratch.
Two training rounds on tens of thousands of real enterprise documents. The model started at 38%, hit 96% on the first production instance, and reached 99% after fine tuning on the errors it actually made. Near infallible at document merging and classification at scale.
Round 1 Accuracy
96%
First production instance
Round 2 Accuracy
99%
After fine tuning on real errors
Documents Processed
50k+
In first deployment batch
Hours Freed Monthly
340+
Employee time reclaimed
Enterprise AI Capabilities
Custom LLM Development
Purpose built language models trained from scratch on your proprietary data and document types.
Document Intelligence
Automated merging, classification, and extraction across contracts, invoices, and legal documents at scale.
Enterprise Security
On premise deployment with zero external data transmission, end to end encryption, and complete audit trail.
Process Automation
End to end document workflow automation replacing manual review across large enterprise teams.
Iterative Model Training
Multiple training rounds using production feedback loops, pushing accuracy from baseline to 99%.
ERP and CRM Integration
Connecting the LLM pipeline to existing enterprise infrastructure without disrupting current workflows.
What we replaced
The document processing workflow at a corporate client, before and after deployment of our custom enterprise LLM, built and trained entirely by our team.
Before
- Time per document batch8 hours
- Team required6 specialist reviewers
- Error rate~15% on complex merges
- Monthly capacity~2,000 documents
- Audit trailManual, incomplete
After our LLM
- Time per document batch12 minutes
- Team required1 quality control reviewer
- Error rateUnder 1%, 99% accuracy
- Monthly capacity50,000+ documents
- Audit trailAutomated log per query
Security Architecture
Common questions
Our custom LLM for a corporate client reached 96% accuracy after the first production training instance and 99% after the second fine tuning round on real world document classification. Starting baseline before training was 38%. The improvement came from iterative fine tuning on actual production errors, not synthetic test data.
On premise deployment only. Zero external data transmission. The system runs entirely within client infrastructure with end to end encryption at rest and in transit, role based access control, per query audit logging, and no telemetry to third parties. Air gapped deployment is available for high security environments.
We have production experience with contracts, invoices, legal documents, and financial records. The model is trained on client specific document types and adapts to proprietary formats, internal terminology, and domain specific classification logic.
We connect the LLM pipeline to existing enterprise infrastructure via API integration. The system ingests documents from existing data sources and writes outputs back to the client's ERP or CRM without disrupting current workflows. Integration scope is mapped during the Discovery and Architecture phase.
Document classification feeds enriched data back into your ad bidding. When an LLM identifies high quality loans, they are sent to Google Ads via offline conversion tracking. The platform trains its bidding algorithm on these high quality signals rather than on form submissions alone. A fintech client using this approach saw cost per acquisition drop 84% while growing the channel from 3% to 15% of total company sales in 12 months. The mechanism is not bigger budgets. It is better signals powering more accurate bidding.
General purpose models are trained on broad, public data. They handle generic tasks well. For regulated industries, they fail. A general LLM cannot learn your proprietary document formats, your company-specific taxonomy, or your exact compliance requirements. Custom models are fine-tuned on your actual production data over multiple rounds. Starting accuracy is typically 40-60%. After two rounds of production training, accuracy reaches 99%. The model learns not just classification logic but compliance violation patterns across your specific markets. A lending platform using a general model would misclassify 15-20% of complex documents. A custom model misclassifies less than 1%. That difference between 85% and 99% accuracy compounds across thousands of documents monthly and directly impacts your compliance audit and your marketing team's confidence in the data.
Yes. The system is trained on policy violation patterns across Google Ads, Meta, and local market regulations. It flags not just violations but the specific policy rule and the exact text that triggered it. In one engagement, we systematized 112 policy violation patterns. The client's open violation count at the end was zero. This is not human review outsourced to AI. It is AI-driven policy classification embedded into your document pipeline. Every violation is caught and logged before it reaches the ad platform.
The timeline is approximately 12-16 weeks. Discovery and architecture typically take 4 weeks: mapping your document types, data flows, integration points, and compliance requirements. Data preparation and training take 4-6 weeks: curating training data, running baseline models, and building your fine-tuning corpus. Validation and security hardening take 3-4 weeks: iterating accuracy, testing edge cases, and hardening on-premise deployment. Full production deployment with monitoring and audit logging happens in the final 2 weeks. This is aggressive for enterprise infrastructure because we do not require extensive stakeholder alignment rounds. We ship, measure, iterate.
Marketing Data Engineering: Infrastructure Behind Performance Teams
Document classification and LLM processing is not marketing theater. It is the data foundation for teams running performance marketing at scale. Our custom LLM architecture integrates upstream into your data pipeline. Raw documents (contracts, invoices, compliance records, customer communications) flow through the classification system and emerge as structured data: extracted entities, metadata, regulatory classifications, risk flags. This enriched data then feeds downstream into your CRM, your analytics infrastructure, and critically, into your ad platforms.
The mechanism is concrete. A B2B fintech client sends contract documents through our LLM pipeline. The model extracts loan terms, borrower entity type, regulatory jurisdiction, and compliance checkpoints. This data automatically syncs to their CRM via API. When an ad platform call arrives (Google Ads, Meta), the enriched contract data becomes bidding signals. Loans from high-confidence jurisdictions bid differently from loans requiring additional review. The result is not just fewer errors. It is better targeting, lower cost per quality lead, and faster sales cycles because the marketing team now has the same legal and compliance context as the underwriting team. Oligamy Software engineered this pipeline. We have built enrichment systems across five operating markets (Poland, Czech Republic, Spain, Mexico, Latvia) where compliance requirements differ materially. The data dictionary is not generic. It respects local lending laws, advertising restrictions, and know-your-customer requirements.
Automated Compliance Classification in Regulated Marketing
Marketing in regulated industries (fintech, lending, cryptocurrency, securities) requires not just accuracy but compliance visibility. Advertising policy violations cascade downstream. A single misfiled loan product can trigger platform suspension. A compliance misclassification can trigger regulatory investigation. Our LLM system is trained not only to classify documents but to flag compliance risk. The model identifies advertising policy violations across multiple jurisdictions and platforms simultaneously. It maps documents against Google Ads policies, Meta compliance rules, and local market regulations. During one engagement, we systematized the classification of 112 policy violation patterns across regulated lending campaigns. The client's open violation count at engagement end was zero. Not lower. Zero.
The mechanism: the fine-tuned model learns not just the content of a policy (no misleading loan terms, no unrealistic APR claims) but the exact language that violates it. When a new document arrives, the system flags not just the violation but the specific policy rule and the exact text that triggered it. This flag streams to the compliance team in real time. Campaigns do not go live until violations are remediated. This is not outsourced compliance. It is embedded AI-driven compliance infrastructure. Your marketing team moves faster because compliance review happens in minutes, not days. Your legal team has complete audit trail because every decision is logged and reasoned.
How We Reach 99% Accuracy: Production Feedback and Iterative Training
Accuracy claims from AI vendors are commoditized. Every vendor claims "enterprise grade" accuracy. The difference is in the training methodology and how violations are measured. Our process is not synthetic. We do not train on test datasets. We train on your production data, measure against your exact classification logic, and iterate based on real errors the model makes in the field.
Start: a baseline model (pre-trained on public financial/legal data) classifies your first batch of documents. Accuracy is typically 38-65% because the model has never seen your taxonomy, your proprietary document formats, or your specific compliance requirements. This is expected. Round one: we take the model's errors on real production documents, categorize them by type (missed entity, wrong classification, compliance misread), and create a fine-tuning corpus. The model trains on this curated error set. Accuracy jumps to 85-96% because the model is now learning your specific patterns, not generic patterns. Round two: production data from live classification flows back into training. The model sees the highest-confidence errors and learns from them specifically. Accuracy reaches 99% because the system is now tuned to the edge cases that matter to your business, not the edge cases that matter to generic benchmarks.
Measurement is concrete. We track accuracy per document category (contracts vs. invoices vs. regulatory filings) and per violation type (compliance vs. classification vs. extraction). If a specific category lags (invoices at 94%, contracts at 99%), we allocate additional training data to that category. This surgical precision is why our models converge to 99% rather than plateauing at 95%. The training infrastructure runs entirely within your environment. No data leaves your premises during training. Audit logs show every training iteration, every accuracy measurement, every data sample used. This is critical for regulated clients who cannot export financial or customer data to third party infrastructure.
Building Custom Infrastructure for High Performance Marketing Teams
Generic AI models are not built for marketing. They classify documents. Custom marketing infrastructure classifies documents and connects those classifications directly to your conversion funnels, your ad bidding, and your revenue measurement. Our LLM system integrates upstream into your data layer and downstream into your marketing stack. Documents flow in. Enriched data flows out to three destinations simultaneously: your CRM (for sales workflow), your data warehouse (for analytics), and your ad platforms (for bidding and segmentation).
Example: a lending platform processes 50,000 documents monthly. Our LLM identifies loan type, borrower risk tier, product category, and regulatory status per document. These signals sync immediately to the CRM via API, updating the lead record. Google Ads receives the enriched data via ConversionUploadService and CRM event triggers (qualified lead, high risk, regulatory hold). The ad platform now bids differently on audiences associated with high risk loans versus standard tier loans. Cost per quality conversion improves because the bidding algorithm has the same classification data as your underwriting team. This infrastructure requires custom engineering. It is not a prebuilt workflow. Your document formats are unique. Your CRM schema is unique. Your ad account structure is unique. We map all three, build the integration points, and test end-to-end with real data before production deployment. Oligamy Software has built this infrastructure for clients across five markets. Poland has different lending regulations than Mexico. Mexico's Meta compliance requirements differ from Latvia's. Our engineering team does not reuse templates. We engineer custom pipelines per client per market, respecting local data protection laws, advertising rules, and compliance requirements.